wakamatsuikuma/01_ps_causal_inference.ipynb

## 01_ps_causal_inference.ipynb
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "IV-No6L5Zr1B",
        "s9WQ74LFZzUi",
        "5jUfYTJvZ7DO",
        "gIxydivSvvSt",
        "CaCV-M9ZaFAK",
        "7CwN91b22yV8"
      ]
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### パッケージのインストール、ライブラリ読み込み、データ読み込み"
      ],
      "metadata": {
        "id": "IV-No6L5Zr1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# havenパッケージのインストール\n",
        "install.packages(\"haven\")\n",
        "install.packages(\"MatchIt\")\n",
        "install.packages(\"WeightIt\")\n",
        "install.packages(\"cobalt\")\n",
        "install.packages(\"psych\")\n",
        "install.packages(\"reshape2\")\n",
        "install.packages(\"pROC\") # ROC曲線描画用\n",
        "\n",
        "# ライブラリの読み込み\n",
        "library(\"tidyverse\")\n",
        "library(\"haven\")\n",
        "library(\"broom\")\n",
        "library(\"MatchIt\")\n",
        "library(\"WeightIt\")\n",
        "library(\"cobalt\")\n",
        "library(\"psych\")\n",
        "library(\"ggplot2\")\n",
        "library(\"reshape2\")\n",
        "library(\"pROC\")"
      ],
      "metadata": {
        "id": "H8ZBULBNsOwO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "520cdd9c-bda2-471c-9469-9bbc1ae6302b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_check <- function(df){\n",
        "  print(\"【形状】:\")\n",
        "  print(dim(df))\n",
        "\n",
        "  print(\"【データ型】:\")\n",
        "  print(sapply(df, class))\n",
        "\n",
        "  print(\"【欠損量】:\")\n",
        "  print(colSums(is.na(df)))\n",
        "\n",
        "  print(\"【ユニーク数】:\")\n",
        "  print(apply(df, 2, function(x) length(unique(x))))\n",
        "\n",
        "  print(\"【先頭行】:\")\n",
        "  print(head(df, n=5))\n",
        "\n",
        "  print(\"【統計量】\")\n",
        "  summary(df)\n",
        "}"
      ],
      "metadata": {
        "id": "PvhEouzZcEAb"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# データ読み込み\n",
        "nswdw_data <- read_dta(\"https://users.nber.org/~rdehejia/data/nsw_dw.dta\")\n",
        "cps1_data <- read_dta(\"https://users.nber.org/~rdehejia/data/cps_controls.dta\")\n",
        "cps3_data <- read_dta(\"https://users.nber.org/~rdehejia/data/cps_controls3.dta\")"
      ],
      "metadata": {
        "id": "m8--dl0wsOy5"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# NSWデータから介入グループだけを取り出して、cps1における介入グループとして扱う\n",
        "cps1_nsw_data <- nswdw_data %>%\n",
        "  filter(treat==1) %>%\n",
        "  dplyr::bind_rows(cps1_data)\n",
        "\n",
        "data_check(cps1_nsw_data)"
      ],
      "metadata": {
        "id": "RIDEWzw3sO1L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "outputId": "903721e0-ca46-4bcf-e618-22182fe528ee"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"【形状】:\"\n",
            "[1] 16177    11\n",
            "[1] \"【データ型】:\"\n",
            "    data_id       treat         age   education       black    hispanic \n",
            "\"character\"   \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\" \n",
            "    married    nodegree        re74        re75        re78 \n",
            "  \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\" \n",
            "[1] \"【欠損量】:\"\n",
            "  data_id     treat       age education     black  hispanic   married  nodegree \n",
            "        0         0         0         0         0         0         0         0 \n",
            "     re74      re75      re78 \n",
            "        0         0         0 \n",
            "[1] \"【ユニーク数】:\"\n",
            "  data_id     treat       age education     black  hispanic   married  nodegree \n",
            "        2         2        40        19         2         2         2         2 \n",
            "     re74      re75      re78 \n",
            "     7385      7688      7383 \n",
            "[1] \"【先頭行】:\"\n",
            "\u001b[90m# A tibble: 5 × 11\u001b[39m\n",
            "  data_id      treat   age education black hispanic married nodegree  re74  re75\n",
            "  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m1\u001b[39m Dehejia-Wah…     1    37        11     1        0       1        1     0     0\n",
            "\u001b[90m2\u001b[39m Dehejia-Wah…     1    22         9     0        1       0        1     0     0\n",
            "\u001b[90m3\u001b[39m Dehejia-Wah…     1    30        12     1        0       0        0     0     0\n",
            "\u001b[90m4\u001b[39m Dehejia-Wah…     1    27        11     1        0       0        1     0     0\n",
            "\u001b[90m5\u001b[39m Dehejia-Wah…     1    33         8     1        0       0        1     0     0\n",
            "\u001b[90m# ℹ 1 more variable: re78 <dbl>\u001b[39m\n",
            "[1] \"【統計量】\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   data_id              treat              age          education    \n",
              " Length:16177       Min.   :0.00000   Min.   :16.00   Min.   : 0.00  \n",
              " Class :character   1st Qu.:0.00000   1st Qu.:24.00   1st Qu.:11.00  \n",
              " Mode  :character   Median :0.00000   Median :31.00   Median :12.00  \n",
              "                    Mean   :0.01144   Mean   :33.14   Mean   :12.01  \n",
              "                    3rd Qu.:0.00000   3rd Qu.:42.00   3rd Qu.:13.00  \n",
              "                    Max.   :1.00000   Max.   :55.00   Max.   :18.00  \n",
              "     black            hispanic          married          nodegree     \n",
              " Min.   :0.00000   Min.   :0.00000   Min.   :0.0000   Min.   :0.0000  \n",
              " 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.0000  \n",
              " Median :0.00000   Median :0.00000   Median :1.0000   Median :0.0000  \n",
              " Mean   :0.08234   Mean   :0.07189   Mean   :0.7058   Mean   :0.3006  \n",
              " 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:1.0000   3rd Qu.:1.0000  \n",
              " Max.   :1.00000   Max.   :1.00000   Max.   :1.0000   Max.   :1.0000  \n",
              "      re74            re75            re78      \n",
              " Min.   :    0   Min.   :    0   Min.   :    0  \n",
              " 1st Qu.: 4075   1st Qu.: 4103   1st Qu.: 5493  \n",
              " Median :14892   Median :14374   Median :16240  \n",
              " Mean   :13880   Mean   :13512   Mean   :14749  \n",
              " 3rd Qu.:23492   3rd Qu.:22830   3rd Qu.:25565  \n",
              " Max.   :35040   Max.   :25244   Max.   :60308  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NSWデータから介入グループだけを取り出して、cps3における介入グループとして扱う\n",
        "cps3_nsw_data <- nswdw_data %>%\n",
        "  filter(treat==1) %>%\n",
        "  dplyr::bind_rows(cps3_data)\n",
        "\n",
        "data_check(cps3_nsw_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "jb9bDt4Pbm6R",
        "outputId": "c88d8481-dc6e-4510-95c0-cc75d0a59e8e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"【形状】:\"\n",
            "[1] 614  11\n",
            "[1] \"【データ型】:\"\n",
            "    data_id       treat         age   education       black    hispanic \n",
            "\"character\"   \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\" \n",
            "    married    nodegree        re74        re75        re78 \n",
            "  \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\"   \"numeric\" \n",
            "[1] \"【欠損量】:\"\n",
            "  data_id     treat       age education     black  hispanic   married  nodegree \n",
            "        0         0         0         0         0         0         0         0 \n",
            "     re74      re75      re78 \n",
            "        0         0         0 \n",
            "[1] \"【ユニーク数】:\"\n",
            "  data_id     treat       age education     black  hispanic   married  nodegree \n",
            "        2         2        40        19         2         2         2         2 \n",
            "     re74      re75      re78 \n",
            "      358       356       457 \n",
            "[1] \"【先頭行】:\"\n",
            "\u001b[90m# A tibble: 5 × 11\u001b[39m\n",
            "  data_id      treat   age education black hispanic married nodegree  re74  re75\n",
            "  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m1\u001b[39m Dehejia-Wah…     1    37        11     1        0       1        1     0     0\n",
            "\u001b[90m2\u001b[39m Dehejia-Wah…     1    22         9     0        1       0        1     0     0\n",
            "\u001b[90m3\u001b[39m Dehejia-Wah…     1    30        12     1        0       0        0     0     0\n",
            "\u001b[90m4\u001b[39m Dehejia-Wah…     1    27        11     1        0       0        1     0     0\n",
            "\u001b[90m5\u001b[39m Dehejia-Wah…     1    33         8     1        0       0        1     0     0\n",
            "\u001b[90m# ℹ 1 more variable: re78 <dbl>\u001b[39m\n",
            "[1] \"【統計量】\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   data_id              treat             age          education    \n",
              " Length:614         Min.   :0.0000   Min.   :16.00   Min.   : 0.00  \n",
              " Class :character   1st Qu.:0.0000   1st Qu.:20.00   1st Qu.: 9.00  \n",
              " Mode  :character   Median :0.0000   Median :25.00   Median :11.00  \n",
              "                    Mean   :0.3013   Mean   :27.36   Mean   :10.27  \n",
              "                    3rd Qu.:1.0000   3rd Qu.:32.00   3rd Qu.:12.00  \n",
              "                    Max.   :1.0000   Max.   :55.00   Max.   :18.00  \n",
              "     black           hispanic         married          nodegree     \n",
              " Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  \n",
              " 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  \n",
              " Median :0.0000   Median :0.0000   Median :0.0000   Median :1.0000  \n",
              " Mean   :0.3958   Mean   :0.1173   Mean   :0.4153   Mean   :0.6303  \n",
              " 3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  \n",
              " Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  \n",
              "      re74            re75              re78        \n",
              " Min.   :    0   Min.   :    0.0   Min.   :    0.0  \n",
              " 1st Qu.:    0   1st Qu.:    0.0   1st Qu.:  238.3  \n",
              " Median : 1042   Median :  601.5   Median : 4759.0  \n",
              " Mean   : 4558   Mean   : 2184.9   Mean   : 6792.8  \n",
              " 3rd Qu.: 7888   3rd Qu.: 3249.0   3rd Qu.:10893.6  \n",
              " Max.   :35040   Max.   :25142.2   Max.   :60307.9  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(head(nswdw_data, 5))\n",
        "print(head(cps1_nsw_data, 5))\n",
        "print(head(cps3_nsw_data, 5))"
      ],
      "metadata": {
        "id": "mNUvHU-KuE2O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4342215-7dbf-47dc-a325-54a2d74ae215"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 5 × 11\u001b[39m\n",
            "  data_id      treat   age education black hispanic married nodegree  re74  re75\n",
            "  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m1\u001b[39m Dehejia-Wah…     1    37        11     1        0       1        1     0     0\n",
            "\u001b[90m2\u001b[39m Dehejia-Wah…     1    22         9     0        1       0        1     0     0\n",
            "\u001b[90m3\u001b[39m Dehejia-Wah…     1    30        12     1        0       0        0     0     0\n",
            "\u001b[90m4\u001b[39m Dehejia-Wah…     1    27        11     1        0       0        1     0     0\n",
            "\u001b[90m5\u001b[39m Dehejia-Wah…     1    33         8     1        0       0        1     0     0\n",
            "\u001b[90m# ℹ 1 more variable: re78 <dbl>\u001b[39m\n",
            "\u001b[90m# A tibble: 5 × 11\u001b[39m\n",
            "  data_id      treat   age education black hispanic married nodegree  re74  re75\n",
            "  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m1\u001b[39m Dehejia-Wah…     1    37        11     1        0       1        1     0     0\n",
            "\u001b[90m2\u001b[39m Dehejia-Wah…     1    22         9     0        1       0        1     0     0\n",
            "\u001b[90m3\u001b[39m Dehejia-Wah…     1    30        12     1        0       0        0     0     0\n",
            "\u001b[90m4\u001b[39m Dehejia-Wah…     1    27        11     1        0       0        1     0     0\n",
            "\u001b[90m5\u001b[39m Dehejia-Wah…     1    33         8     1        0       0        1     0     0\n",
            "\u001b[90m# ℹ 1 more variable: re78 <dbl>\u001b[39m\n",
            "\u001b[90m# A tibble: 5 × 11\u001b[39m\n",
            "  data_id      treat   age education black hispanic married nodegree  re74  re75\n",
            "  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m1\u001b[39m Dehejia-Wah…     1    37        11     1        0       1        1     0     0\n",
            "\u001b[90m2\u001b[39m Dehejia-Wah…     1    22         9     0        1       0        1     0     0\n",
            "\u001b[90m3\u001b[39m Dehejia-Wah…     1    30        12     1        0       0        0     0     0\n",
            "\u001b[90m4\u001b[39m Dehejia-Wah…     1    27        11     1        0       0        1     0     0\n",
            "\u001b[90m5\u001b[39m Dehejia-Wah…     1    33         8     1        0       0        1     0     0\n",
            "\u001b[90m# ℹ 1 more variable: re78 <dbl>\u001b[39m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RCTでの評価"
      ],
      "metadata": {
        "id": "s9WQ74LFZzUi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# RCTデータであるnswdw_dataで回帰分析\n",
        "\n",
        "# ナイーブな推定: グループ間の平均の比較\n",
        "tau_nsw_naive <- mean(filter(nswdw_data, treat==1)$re78) - mean(filter(nswdw_data, treat==0)$re78)\n",
        "paste(\"nswdw_dataのナイーブな推定効果: \", tau_nsw_naive)\n",
        "\n",
        "# 共変量なしの回帰分析\n",
        "nsw_nocov <- lm(data = nswdw_data,\n",
        "formula = re78 ~ treat) %>%\n",
        "  tidy() # t検定などの結果を省略。各変数について、推定効果、標準誤差、t値, p値。\n",
        "print(nsw_nocov)\n",
        "\n",
        "# 共変量ありの回帰分析\n",
        "nsw_cov <- lm(data = nswdw_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75) %>%\n",
        "  tidy() # t検定などの結果を省略\n",
        "print(nsw_cov)\n",
        "\n",
        "# 共変量ありに注目。介入により$1,676の収入アップ効果があることを示す。\n",
        "# 統計的な妥当性としては、有意水準0.05 > p値0.00898であり、有意であると判断する。"
      ],
      "metadata": {
        "id": "5iJjPYXkuE5D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "7c6099d1-3fe1-49dc-ee4d-1b61147ff8e0"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=white-space:pre-wrap>'nswdw_dataのナイーブな推定効果:  1794.3423818501'</span>"
            ],
            "text/markdown": "<span style=white-space:pre-wrap>'nswdw_dataのナイーブな推定効果:  1794.3423818501'</span>",
            "text/latex": "'nswdw\\_dataのナイーブな推定効果:  1794.3423818501'",
            "text/plain": [
              "[1] \"nswdw_dataのナイーブな推定効果:  1794.3423818501\""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 2 × 5\u001b[39m\n",
            "  term        estimate std.error statistic  p.value\n",
            "  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m1\u001b[39m (Intercept)    \u001b[4m4\u001b[24m555.      408.     11.2  1.15\u001b[90me\u001b[39m\u001b[31m-25\u001b[39m\n",
            "\u001b[90m2\u001b[39m treat          \u001b[4m1\u001b[24m794.      633.      2.84 4.79\u001b[90me\u001b[39m\u001b[31m- 3\u001b[39m\n",
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term          estimate std.error statistic p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept)   785.     \u001b[4m3\u001b[24m375.        0.233  0.816  \n",
            "\u001b[90m 2\u001b[39m treat        \u001b[4m1\u001b[24m676.      639.        2.62   0.008\u001b[4m9\u001b[24m\u001b[4m8\u001b[24m\n",
            "\u001b[90m 3\u001b[39m age            55.3      45.3       1.22   0.223  \n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m\u001b[4m2\u001b[24m16\u001b[39m\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m     \u001b[4m1\u001b[24m169.       -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m85\u001b[39m   0.065\u001b[4m4\u001b[24m \n",
            "\u001b[90m 5\u001b[39m hispanic      164.     \u001b[4m1\u001b[24m549.        0.106  0.916  \n",
            "\u001b[90m 6\u001b[39m married      -\u001b[31m139\u001b[39m\u001b[31m.\u001b[39m      880.       -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m158\u001b[39m  0.875  \n",
            "\u001b[90m 7\u001b[39m nodegree      -\u001b[31m70\u001b[39m\u001b[31m.\u001b[39m\u001b[31m7\u001b[39m    \u001b[4m1\u001b[24m004.       -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m0\u001b[39m\u001b[31m70\u001b[4m4\u001b[24m\u001b[39m 0.944  \n",
            "\u001b[90m 8\u001b[39m education     396.      227.        1.74   0.082\u001b[4m5\u001b[24m \n",
            "\u001b[90m 9\u001b[39m re74            0.082\u001b[4m1\u001b[24m    0.077\u001b[4m4\u001b[24m    1.06   0.289  \n",
            "\u001b[90m10\u001b[39m re75            0.052\u001b[4m8\u001b[24m    0.135     0.389  0.697  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### バイアスデータで回帰分析"
      ],
      "metadata": {
        "id": "5jUfYTJvZ7DO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ナイーブな推定: グループ間の平均の比較\n",
        "tau_cps1_naive <- mean(filter(cps1_nsw_data, treat==1)$re78) - mean(filter(cps1_nsw_data, treat==0)$re78)\n",
        "tau_cps3_naive <- mean(filter(cps3_nsw_data, treat==1)$re78) - mean(filter(cps3_nsw_data, treat==0)$re78)\n",
        "\n",
        "paste(\"cps1_nsw_dataのナイーブな推定効果: \", tau_cps1_naive)\n",
        "paste(\"cps3_nsw_dataのナイーブな推定効果: \", tau_cps3_naive)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "g1O00YldZ-lC",
        "outputId": "c7aebfcc-72fa-48a3-ffdf-48145673e45f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=white-space:pre-wrap>'cps1_nsw_dataのナイーブな推定効果:  -8497.51614813298'</span>"
            ],
            "text/markdown": "<span style=white-space:pre-wrap>'cps1_nsw_dataのナイーブな推定効果:  -8497.51614813298'</span>",
            "text/latex": "'cps1\\_nsw\\_dataのナイーブな推定効果:  -8497.51614813298'",
            "text/plain": [
              "[1] \"cps1_nsw_dataのナイーブな推定効果:  -8497.51614813298\""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=white-space:pre-wrap>'cps3_nsw_dataのナイーブな推定効果:  -635.026250406911'</span>"
            ],
            "text/markdown": "<span style=white-space:pre-wrap>'cps3_nsw_dataのナイーブな推定効果:  -635.026250406911'</span>",
            "text/latex": "'cps3\\_nsw\\_dataのナイーブな推定効果:  -635.026250406911'",
            "text/plain": [
              "[1] \"cps3_nsw_dataのナイーブな推定効果:  -635.026250406911\""
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 回帰分析\n",
        "cps1_reg <- lm(data = cps1_nsw_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75) %>%\n",
        "    tidy()\n",
        "\n",
        "cps3_reg <- lm(data = cps3_nsw_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75) %>%\n",
        "    tidy()\n",
        "\n",
        "print(cps1_reg)\n",
        "print(cps3_reg)\n",
        "\n",
        "# cps1_nsw_dataの介入効果は$699とRCTと比べてかなり差がある\n",
        "# 介入データがかなり少ないこと および データの分布が異なる。\n",
        "# NSWはそもそも介入(職業訓練)の必要性がある人たちを対象にRCTを行ったが、CPS1はそうでは無い。\n",
        "# そのため、類似サンプル内において介入・非介入の偏りがあったり、\n",
        "# サンプル間で効果が非線形であることが考えられる。\n",
        "# つまり、効果推定を行うデータセットの分布が異なるため乖離が大きいと考えられる。\n",
        "# この状態のデータセットでは、回帰分析の計算の仕組み上うまく推定ができない。\n",
        "# その介入によって、どういった特徴や前提のあるデータを対象に効果推定したいのかを設定して、\n",
        "# それに沿う様にデータセットの作成・準備、分析をする必要がある。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwhQRcg6aAM3",
        "outputId": "11cd4a71-25ba-4da7-a781-2789d0e51db6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term        estimate std.error statistic   p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept) \u001b[4m5\u001b[24m736.     445.        12.9   8.48\u001b[90me\u001b[39m\u001b[31m- 38\u001b[39m\n",
            "\u001b[90m 2\u001b[39m treat        699.     548.         1.28  2.02\u001b[90me\u001b[39m\u001b[31m-  1\u001b[39m\n",
            "\u001b[90m 3\u001b[39m age         -\u001b[31m102\u001b[39m\u001b[31m.\u001b[39m       5.88     -\u001b[31m17\u001b[39m\u001b[31m.\u001b[39m\u001b[31m3\u001b[39m   1.33\u001b[90me\u001b[39m\u001b[31m- 66\u001b[39m\n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m837\u001b[39m\u001b[31m.\u001b[39m     213.        -\u001b[31m3\u001b[39m\u001b[31m.\u001b[39m\u001b[31m93\u001b[39m  8.44\u001b[90me\u001b[39m\u001b[31m-  5\u001b[39m\n",
            "\u001b[90m 5\u001b[39m hispanic    -\u001b[31m218\u001b[39m\u001b[31m.\u001b[39m     219.        -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m998\u001b[39m 3.18\u001b[90me\u001b[39m\u001b[31m-  1\u001b[39m\n",
            "\u001b[90m 6\u001b[39m married       73.1    142.         0.513 6.08\u001b[90me\u001b[39m\u001b[31m-  1\u001b[39m\n",
            "\u001b[90m 7\u001b[39m nodegree     372.     178.         2.10  3.61\u001b[90me\u001b[39m\u001b[31m-  2\u001b[39m\n",
            "\u001b[90m 8\u001b[39m education    160.      28.6        5.60  2.15\u001b[90me\u001b[39m\u001b[31m-  8\u001b[39m\n",
            "\u001b[90m 9\u001b[39m re74           0.289    0.012\u001b[4m1\u001b[24m    24.0   1.26\u001b[90me\u001b[39m\u001b[31m-124\u001b[39m\n",
            "\u001b[90m10\u001b[39m re75           0.471    0.012\u001b[4m2\u001b[24m    38.7   2.28\u001b[90me\u001b[39m\u001b[31m-313\u001b[39m\n",
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term         estimate std.error statistic     p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept)    66.5   \u001b[4m2\u001b[24m437.        0.027\u001b[4m3\u001b[24m 0.978      \n",
            "\u001b[90m 2\u001b[39m treat        \u001b[4m1\u001b[24m548.     781.        1.98   0.048\u001b[4m0\u001b[24m     \n",
            "\u001b[90m 3\u001b[39m age            13.0     32.5       0.399  0.690      \n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m\u001b[4m1\u001b[24m24\u001b[39m\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m     769.       -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m61\u001b[39m   0.107      \n",
            "\u001b[90m 5\u001b[39m hispanic      499.     942.        0.530  0.597      \n",
            "\u001b[90m 6\u001b[39m married       407.     695.        0.585  0.559      \n",
            "\u001b[90m 7\u001b[39m nodegree      260.     847.        0.307  0.759      \n",
            "\u001b[90m 8\u001b[39m education     404.     159.        2.54   0.011\u001b[4m3\u001b[24m     \n",
            "\u001b[90m 9\u001b[39m re74            0.296    0.058\u001b[4m3\u001b[24m    5.09   0.000\u001b[4m0\u001b[24m\u001b[4m0\u001b[24m\u001b[4m0\u001b[24m489\n",
            "\u001b[90m10\u001b[39m re75            0.232    0.105     2.21   0.027\u001b[4m3\u001b[24m     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 傾向スコアの集計と可視化"
      ],
      "metadata": {
        "id": "gIxydivSvvSt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# glmで傾向スコア算出\n",
        "ps_model_cps1 <- glm(data = cps1_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  family = binomial # ロジスティック回帰を指定\n",
        "  )\n",
        "summary(ps_model_cps1)\n",
        "\n",
        "ps_model_cps3 <- glm(data = cps3_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  family = binomial # ロジスティック回帰を指定\n",
        "  )\n",
        "summary(ps_model_cps3)\n",
        "\n",
        "# fitted.valuesで傾向スコア取得\n",
        "ps_cps1 <- ps_model_cps1$fitted.values\n",
        "ps_cps3 <- ps_model_cps3$fitted.values\n",
        "\n",
        "# データフレームに傾向スコア追加\n",
        "cps1_nsw_data$ps <- c(ps_cps1)\n",
        "cps3_nsw_data$ps <- c(ps_cps3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        },
        "id": "Ml4COdag24lf",
        "outputId": "c292f24f-4221-4e30-9a46-56fb59106f16"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "glm(formula = treat ~ age + black + hispanic + married + nodegree + \n",
              "    education + re74 + re75, family = binomial, data = cps1_nsw_data)\n",
              "\n",
              "Coefficients:\n",
              "              Estimate Std. Error z value Pr(>|z|)    \n",
              "(Intercept) -5.605e+00  7.799e-01  -7.187 6.61e-13 ***\n",
              "age         -3.778e-03  9.794e-03  -0.386    0.700    \n",
              "black        4.207e+00  2.579e-01  16.315  < 2e-16 ***\n",
              "hispanic     1.794e+00  3.909e-01   4.589 4.46e-06 ***\n",
              "married     -9.899e-01  2.346e-01  -4.219 2.45e-05 ***\n",
              "nodegree     1.036e+00  2.640e-01   3.923 8.75e-05 ***\n",
              "education    3.458e-02  4.690e-02   0.737    0.461    \n",
              "re74        -2.925e-05  2.714e-05  -1.078    0.281    \n",
              "re75        -2.118e-04  3.591e-05  -5.897 3.70e-09 ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "(Dispersion parameter for binomial family taken to be 1)\n",
              "\n",
              "    Null deviance: 2022.1  on 16176  degrees of freedom\n",
              "Residual deviance: 1004.1  on 16168  degrees of freedom\n",
              "AIC: 1022.1\n",
              "\n",
              "Number of Fisher Scoring iterations: 10\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "glm(formula = treat ~ age + black + hispanic + married + nodegree + \n",
              "    education + re74 + re75, family = binomial, data = cps3_nsw_data)\n",
              "\n",
              "Coefficients:\n",
              "              Estimate Std. Error z value Pr(>|z|)    \n",
              "(Intercept) -4.729e+00  1.017e+00  -4.649 3.33e-06 ***\n",
              "age          1.578e-02  1.358e-02   1.162  0.24521    \n",
              "black        3.065e+00  2.865e-01  10.699  < 2e-16 ***\n",
              "hispanic     9.836e-01  4.257e-01   2.311  0.02084 *  \n",
              "married     -8.321e-01  2.903e-01  -2.866  0.00415 ** \n",
              "nodegree     7.073e-01  3.377e-01   2.095  0.03620 *  \n",
              "education    1.613e-01  6.513e-02   2.477  0.01325 *  \n",
              "re74        -7.178e-05  2.875e-05  -2.497  0.01253 *  \n",
              "re75         5.345e-05  4.635e-05   1.153  0.24884    \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "(Dispersion parameter for binomial family taken to be 1)\n",
              "\n",
              "    Null deviance: 751.49  on 613  degrees of freedom\n",
              "Residual deviance: 487.84  on 605  degrees of freedom\n",
              "AIC: 505.84\n",
              "\n",
              "Number of Fisher Scoring iterations: 5\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ヒストグラム描画\n",
        "ggplot(cps1_nsw_data, aes(x=ps, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=0.05) +\n",
        "  scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "ggplot(cps3_nsw_data, aes(x=ps, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=0.05) +\n",
        "  # scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "# マッチングだと傾向スコアが高い or 低い人たちが除かれてしまうがいいのか"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "pjz6XRqOrtZu",
        "outputId": "8b5507a6-415c-4e60-f4ca-e86e94437805"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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EGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA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\nAERC2AEARCLJZrPdPQa6yDPPPLNx4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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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6oTZFSpUiU1NdWu/44dO6qd/Otf/7p1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\n"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# c統計量(AUC)\n",
        "# ROC曲線で評価\n",
        "ROC1 <- roc(treat ~ ps, data=cps1_nsw_data)\n",
        "ROC3 <- roc(treat ~ ps, data=cps3_nsw_data)\n",
        "\n",
        "# AUC出力\n",
        "print(ROC1)\n",
        "print(ROC3)\n",
        "\n",
        "# ROC曲線描画\n",
        "plot(ROC1)\n",
        "plot(ROC3)\n",
        "\n",
        "# cps1の方がAUCが高く、一見分類機として性能が良さそうだが\n",
        "# cps1_nsw_dataの方がtreat1の精度が低いが、treat0が傾向スコアが低いところに\n",
        "# 集中していて精度が良い(データの偏り的に当たり前といえば当たり前な気もする)ので、\n",
        "# ヒストグラムからくる直感とは異なる結果になった。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SNO7Xn5Rv_A9",
        "outputId": "2a156fbe-8ddc-4d39-a68e-5c7354b842f1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting levels: control = 0, case = 1\n",
            "\n",
            "Setting direction: controls < cases\n",
            "\n",
            "Setting levels: control = 0, case = 1\n",
            "\n",
            "Setting direction: controls < cases\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call:\n",
            "roc.formula(formula = treat ~ ps, data = cps1_nsw_data)\n",
            "\n",
            "Data: ps in 15992 controls (treat 0) < 185 cases (treat 1).\n",
            "Area under the curve: 0.9709\n",
            "\n",
            "Call:\n",
            "roc.formula(formula = treat ~ ps, data = cps3_nsw_data)\n",
            "\n",
            "Data: ps in 429 controls (treat 0) < 185 cases (treat 1).\n",
            "Area under the curve: 0.8742\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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GrAicWGdLPIrWGXgVcc78hsSH1OaTke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\n+WmLyo2F1GH+hwNkzM9OrOjyD0vzVX3+mQ7po65fXda5dUjXyP+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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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PJz7+WY43tP6W7kdsd5y6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\nIBEpCEhECgISkYKARKQgIBEpCEhECvr/8U8+MNF3/XMAAAAASUVORK5CYII="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### バイアスデータに傾向スコアマッチングを用いたATTの推定\n"
      ],
      "metadata": {
        "id": "CaCV-M9ZaFAK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# glm()で算出した傾向スコアと差があるか確認。\n",
        "\n",
        "# matchitで傾向スコア算出\n",
        "m_near_cps1 <- matchit(data = cps1_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  method=\"nearest\")\n",
        "cps1_nsw_data$ps_match <- c(m_near_cps1$distance)\n",
        "\n",
        "# glm()で算出した傾向スコアとの差\n",
        "sum(abs(cps1_nsw_data$ps - cps1_nsw_data$ps_match))\n",
        "\n",
        "# OK"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "9fcNleblT-ad",
        "outputId": "97916981-cd1d-4eff-dd2f-fd86a87827fc"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "0"
            ],
            "text/markdown": "0",
            "text/latex": "0",
            "text/plain": [
              "[1] 0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# バランシングの評価\n",
        "love.plot(m_near_cps1, threshold = 0.1, binary=\"std\")\n",
        "\n",
        "# 今回のマッチング方法である最近隣法マッチングによる非復元1対1マッチングは批判されているが、\n",
        "# バランシングが悪くないのでOKと考える"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "44KltmB1Jog2",
        "outputId": "dded0fbc-785a-4c7a-dd9d-5685de783f12"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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kGwAwAAUAiCHQAAgEIQ7AAAABSCYAcA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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ASAMの検算用の関数\n",
        "standardized_mean_error <- function(df, col){\n",
        "\n",
        "  # ベクトルで値を抽出\n",
        "  values_treat0 <- filter(df, treat==0)[[col]]\n",
        "  values_treat1 <- filter(df, treat==1)[[col]]\n",
        "\n",
        "  # サンプルサイズ\n",
        "  n_0 <- length(values_treat0)\n",
        "  n_1 <- length(values_treat1)\n",
        "\n",
        "  # 平均\n",
        "  mean_0 <- mean(values_treat0)\n",
        "  mean_1 <- mean(values_treat1)\n",
        "\n",
        "  # 分散\n",
        "  s2 <- function(x){ var(x)*(length(x)-1)/length(x) }\n",
        "  var_0 <- s2(values_treat0)\n",
        "  var_1 <- s2(values_treat1)\n",
        "\n",
        "  # 標準平均化誤差\n",
        "  m_diff <- mean_1 - mean_0\n",
        "  s_co <- (var_1*(n_1 - 1) + var_0*(n_0 - 1)) / (n_1 + n_0 - 2)\n",
        "\n",
        "  return ( m_diff / sqrt(s_co) )\n",
        "}"
      ],
      "metadata": {
        "id": "1bC0B4B3MEc4"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 調整後データ\n",
        "matched1_data <- match.data(m_near_cps1)\n",
        "\n",
        "cols_li <- c(\"age\", \"black\", \"hispanic\", \"married\", \"nodegree\", \"education\", \"re74\", \"re75\")\n",
        "for (col in cols_li){\n",
        "  print(paste(col, \"のASAM\"))\n",
        "  print(paste(\"調整前: \", standardized_mean_error(cps1_nsw_data, col)))\n",
        "  print(paste(\"調整後: \", standardized_mean_error(matched1_data, col)))\n",
        "}\n",
        "\n",
        "# 検算したがlove.plotと合わない\n",
        "# いくつかドキュメントも確認したが、数式が見つからず確かめられなかった。\n",
        "# 調整後の方が小さいのでとりあえず今回はOK\n",
        "# 調整はうまくできている"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfzT8-9bVlQ6",
        "outputId": "52fa03e6-640c-436a-94b4-daa7ccad5eec"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"age のASAM\"\n",
            "[1] \"調整前:  -0.673045077618078\"\n",
            "[1] \"調整後:  0.2435367704803\"\n",
            "[1] \"black のASAM\"\n",
            "[1] \"調整前:  2.93324709421367\"\n",
            "[1] \"調整後:  0.0437130189471938\"\n",
            "[1] \"hispanic のASAM\"\n",
            "[1] \"調整前:  -0.0486883897274181\"\n",
            "[1] \"調整後:  0.0735214622093808\"\n",
            "[1] \"married のASAM\"\n",
            "[1] \"調整前:  -1.1552824924244\"\n",
            "[1] \"調整後:  0.147042924418762\"\n",
            "[1] \"nodegree のASAM\"\n",
            "[1] \"調整前:  0.903320101340552\"\n",
            "[1] \"調整後:  0.0587058294730288\"\n",
            "[1] \"education のASAM\"\n",
            "[1] \"調整前:  -0.587471521697691\"\n",
            "[1] \"調整後:  -0.00913489647323476\"\n",
            "[1] \"re74 のASAM\"\n",
            "[1] \"調整前:  -1.25103406168653\"\n",
            "[1] \"調整後:  0.0462501126863007\"\n",
            "[1] \"re75 のASAM\"\n",
            "[1] \"調整前:  -1.31388559386733\"\n",
            "[1] \"調整後:  0.0834656684538306\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cps1_nsw_dataの分析\n",
        "# 調整後データでヒストグラム描画\n",
        "\n",
        "# 傾向スコア\n",
        "ggplot(matched1_data, aes(x=ps, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=0.05) +\n",
        "  scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "# re78\n",
        "ggplot(matched1_data, aes(x=re78, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=10000) +\n",
        "  # scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "# 回帰分析で効果推定(解析モデルにも共変量を使用)\n",
        "psm1_co_result <- lm(data = matched1_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75) %>%\n",
        "  tidy()\n",
        "\n",
        "print(psm1_co_result)\n",
        "\n",
        "# 介入により$1,625の収入アップ効果があることを示しており、RCTの$1,676と近い値。\n",
        "# 調整前の$699と比べてかなり改善\n",
        "# 統計的な妥当性としては、有意水準0.05 > p値0.0180であり、優位であると判断する。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UvftHuiPMEmd",
        "outputId": "e6a22d9d-97aa-4003-f805-4df5ae7accb0"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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nTQVvLss8/euXPH8ugNHTpUCDFs2LBa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\ngg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term         estimate std.error statistic p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept)  426.      \u001b[4m3\u001b[24m479.       0.122   0.903 \n",
            "\u001b[90m 2\u001b[39m treat       \u001b[4m1\u001b[24m625.       721.       2.26    0.024\u001b[4m7\u001b[24m\n",
            "\u001b[90m 3\u001b[39m age           -\u001b[31m9\u001b[39m\u001b[31m.\u001b[39m\u001b[31m10\u001b[39m      48.3     -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m188\u001b[39m   0.851 \n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m746\u001b[39m\u001b[31m.\u001b[39m      \u001b[4m1\u001b[24m150.      -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m649\u001b[39m   0.517 \n",
            "\u001b[90m 5\u001b[39m hispanic    \u001b[4m1\u001b[24m504.      \u001b[4m1\u001b[24m933.       0.778   0.437 \n",
            "\u001b[90m 6\u001b[39m married      361.      \u001b[4m1\u001b[24m062.       0.340   0.734 \n",
            "\u001b[90m 7\u001b[39m nodegree     274.      \u001b[4m1\u001b[24m104.       0.248   0.804 \n",
            "\u001b[90m 8\u001b[39m education    421.       215.       1.96    0.051\u001b[4m0\u001b[24m\n",
            "\u001b[90m 9\u001b[39m re74          -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m0\u001b[39m\u001b[31m11\u001b[4m0\u001b[24m\u001b[39m     0.111   -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m0\u001b[39m\u001b[31m99\u001b[4m6\u001b[24m\u001b[39m  0.921 \n",
            "\u001b[90m10\u001b[39m re75           0.312      0.165    1.89    0.059\u001b[4m8\u001b[24m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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nniiSdu3bplufeGDBkihBg+fHitWrXk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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# m_near_cps3についても傾向スコアマッチング\n",
        "m_near_cps3 <- matchit(data = cps3_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  method=\"nearest\")\n",
        "\n",
        "# 調整後データ\n",
        "matched3_data <- match.data(m_near_cps3)\n",
        "\n",
        "# 調整後データでヒストグラム描画\n",
        "\n",
        "# 傾向スコア\n",
        "ggplot(matched3_data, aes(x=ps, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=0.05) +\n",
        "  # scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "# re78\n",
        "ggplot(matched3_data, aes(x=re78, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=10000) +\n",
        "  # scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "# 回帰分析(解析モデルにも共変量を使用)\n",
        "psm3_co_result <- lm(data = matched3_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75) %>%\n",
        "  tidy()\n",
        "\n",
        "print(psm3_co_result)\n",
        "\n",
        "# バランシングの評価\n",
        "love.plot(m_near_cps3, threshold = 0.1, binary=\"std\")\n",
        "\n",
        "# cps3_nsw_dataは回帰分析と比べて結果が改悪\n",
        "# よいマッチング相手がなかったのかもしれない。というのは、\n",
        "#   調整前データ自体がかなり絞られていた\n",
        "#   調整前の傾向スコアの高い領域で介入群の方が多く、傾向スコアの似たサンプルと一致の具合が十分で無かった\n",
        "# 対照群もcps1と比べると、少ない別データからマッチングさせたものであり、cps3データ作成時の抽出基準が\n",
        "# そこまで適しているわけでなかったのかもしれない\n",
        "# -> 傾向スコアマッチングでのATT推定は、対照群のデータが豊富で多様性のある時の方が良いかもしれない"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gr_0Iz6KMErt",
        "outputId": "caaad840-d6fd-4fd3-9ac0-9daea3e65cbf"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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RgxYvLkyU6sFAAAwF24abBbuXJlamqql5dXdnZ24bVfffXVqVOnPD09w8LCevbs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\nhaiSYwAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term          estimate std.error statistic p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept) -\u001b[31m\u001b[4m2\u001b[24m11\u001b[39m\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m      \u001b[4m3\u001b[24m512.       -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m601\u001b[39m 0.548  \n",
            "\u001b[90m 2\u001b[39m treat        \u001b[4m1\u001b[24m345.       790.        1.70  0.089\u001b[4m4\u001b[24m \n",
            "\u001b[90m 3\u001b[39m age             7.80      42.9       0.182 0.856  \n",
            "\u001b[90m 4\u001b[39m black        -\u001b[31m469\u001b[39m\u001b[31m.\u001b[39m      \u001b[4m1\u001b[24m010.       -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m465\u001b[39m 0.642  \n",
            "\u001b[90m 5\u001b[39m hispanic     \u001b[4m1\u001b[24m064.      \u001b[4m1\u001b[24m300.        0.818 0.414  \n",
            "\u001b[90m 6\u001b[39m married      -\u001b[31m158\u001b[39m\u001b[31m.\u001b[39m       986.       -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m160\u001b[39m 0.873  \n",
            "\u001b[90m 7\u001b[39m nodegree      923.      \u001b[4m1\u001b[24m110.        0.832 0.406  \n",
            "\u001b[90m 8\u001b[39m education     602.       224.        2.69  0.007\u001b[4m5\u001b[24m\u001b[4m3\u001b[24m\n",
            "\u001b[90m 9\u001b[39m re74            0.026\u001b[4m4\u001b[24m     0.103     0.256 0.798  \n",
            "\u001b[90m10\u001b[39m re75            0.221      0.160     1.38  0.168  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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ATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDY\nAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAA\naATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBG+Hm7AJctXrx47969jtuMGjVq1KhR8vK0adMu\nXrxYVMsePXrMmjXLg+UBAAB4i+8FO6PRGBgYWNTa7OzsgoICne7/H4nMyMiQt9Lr9XZ7U6NI\nAACAkud7we6JJ5544okn7K76+++/Z8yYYTAYevbsqbyYnp4uhHjmmWfatWtXQiUCAAB4g3au\nsbNYLO+9915+fv6YMWMqVqwov1hQUJCVlSWECAoK8mp1AAAAqtNOsNu6deuff/5Zv379QYMG\nKS/Kw3VCiODgYC/VBQAAUEI0EuwSExM3bNig1+ufeuopSZKU1+UL7AQjdgAA4F/A966xs2vV\nqlU5OTkDBgyoXbu29evKiF1+fv6mTZuOHz+enJxsMBiqV6/epUuXDh06WKdAAAAAn6aFYHfy\n5MnDhw8HBgY+/PDDNquUYDdjxozMzEzl9QsXLsTGxkZFRc2dO7fwWdq9e/cWFBTIy9WrV69W\nrZo75amYHSV1+1fvlmG9Xq/T6XzulmR5V/ti5UIISZIMBoO3q3CZn5+fEMLf39/bhbhMp9P5\n6KEizypQaiu3O78BAIUWgt2GDRuEEIMGDQoJCbFZpQS7cuXKRUdHN2vWLCgo6Nq1a5s3b967\nd++JEycWLVo0f/58m63mzZuXn58vL48YMeK5555zpzzruVfUoF7/hfenZ5lMJlX7V4mfn5/a\ne0YlPlq2EMJkMvno0eK7+7zUVq78cQZgl88Hu9OnT//+++8Gg8H6nglFo0aN5s2bp9PpWrRo\noQxX1KhRY8aMGeHh4TExMb/88suJEyeioqKst5ozZ44yYle3bl0lHRaP0pXH6XSSEJJ6/bv5\nwR0wGAwWiyUvL0+l/lUiSVJQUFB+fn52dra3a3FZYGBgVlaWxWLxdiGu8ff3NxqN2dnZPvd1\nLg/XyXfl+5aAgAC9Xq/er7+b9Hq9PI4LwC6f//X49ttvhRCdOnUqU6ZM4bUVKlSoUKGC3Q1H\njhy5c+fO9PT0w4cP2wS7IUOGWP8zKSnJnQpV/Cq1SEJSsX/14otOpysoKPC5eKTT6YKCgnyx\nciGEyWTKzs72uWAnSZLRaMzLy8vJyfF2La7R6/X+/v6+eKjI07mX2spL7TlioPTlxB8AACAA\nSURBVJTw7btiMzIyDh8+LIS45557XN3WYDDId1rcvHnT44UBAACUPN8OdkeOHMnNzTWZTJGR\nkcXYXD65w6g+AADQBt/ONEeOHBFCREZGFhXO4uLirly5UqNGjfbt29usys3NvXjxohDCzZte\nAQAASgnfDnZnzpwRQtStW7eoBnFxcfv27atQoUJUVFRgYKD1qi+//FK+iKRw5gMAAPBFPnwq\nNjMz88aNG0KI6tWrF9Vm4MCBkiTdvHlzwYIF586dk1/MysravHlzTEyMEKJr164RERElUzAA\nAICqfHjE7tatW/JCaGhoUW0aNGjw5JNPrlix4o8//pg5c2ZISIjRaExOTjabzUKINm3aPPXU\nUyVULgAAgMp8ONgpT5IICAhw0KxPnz5NmjTZvn17QkJCUlJSVlZWaGhogwYNevXq1a5dOx4p\nBgAANMOHg13Dhg2/+eYbZ1rWqFHjiSeeULseAAAA7/Lha+wAAABgjWAHAACgEQQ7AAAAjSDY\nAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAA\naATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATB\nDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAA\nQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMI\ndgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCP8vF0A\nSq+nryaq1LOfn5/FYjGbzSr1v6hqJZV6BgCgNGPEDgAAQCMIdgAAABpBsAMAANAIgh0AAIBG\nEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwA\nAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBG+Hm7AB9Q\nrlw5dzbX6/WeqqSE+/c3mVTq+f/69/dXqWc3f2SOGQwGVftXiSRJ4eHh3q6imIKDg4ODg71d\nhcskSfLRQ0Wo/Evkjvz8fG+XAJRqBLu7u3Xrljubm81mT1ViQ6/TCUlSr//c7GyVevbz87NY\nLOpV7uaPrCg6nS48PDw3Nzc1NVWN/lUVFhaWkpJisVi8XYhrAgICgoKC0tPTc3JyvF2La/R6\nfXBwcEpKircLcVloaKi/v79Kv0TuMxqN6v2fENAATsUCAABoBMEOAABAIwh2AAAAGkGwAwAA\n0AiCHQAAgEYQ7AAAADSCYAcAAKARBDsAAACNINgBAABoBMEOAABAIwh2AACULp9//nmnTp3K\nlCnj7+9foUKFH3/80dsVecHnn38uSZIkSa+88oq3a/GYDz/8UP5Q77zzjkpvQbADAKAU+fDD\nDx9++OG4uLi0tLT8/PykpCRffOiwm+Li4iZMmCCEGDFixIsvvujtcjzmsccei46OFkI899xz\nW7duVeMtCHYAAJQiixcvlhe6d+/+6aeffvHFFy1btiz5Mh5//HFJkt58882Sf+uUlJRRo0bl\n5OTUrFnzo48+KiVVFYPdahctWtS0adOCgoIJEyZcvnzZ42/q5/EeAQBA8VgslnPnzgkhDAbD\nli1bwsLCvFVJfHy8t956+vTply5dEkKsWrUqNDTUepUXqyoGu9Uajca1a9e2bds2OTl5woQJ\nP/zwg2fflGCHIukv/61Sz5JOEhaht1hU6l9UraRWzwCgpszMzNzcXCFExYoVvZjqMjMzT548\n6ZW3Pnz48Nq1a4UQgwYN6t27dympqhgcVNuqVavx48d/8sknu3fv/uqrr4YNG+bB9+VULAAA\npYXlf//j1ev1Xizjl19+yc/P98pbP/vss/JOeP31121WebGqYnBc7SuvvGIwGIQQc+bMKSgo\n8OD7EuwAAPg/mZmZK1asGDhwYM2aNYOCguSbUrt27frqq6/evHmzqK3MZvNnn332wAMP1KtX\nLzg42M/PLywsrEWLFlOnTv3111+dfOs5c+ZIkhQSEiL/89KlS9L/bNmyxc0KZfv37588eXKD\nBg1CQkKCgoIaNGjw2GOP/fbbb9ZtFixYIElSt27d5H/OnTtXrqFv3742vf3444+TJ09u3Lhx\nWFiYwWCoXLlyx44dX3jhhX/++cfuu3ft2lWSJJ1OZ7FY0tPTp0+fXrFiRaPR+Oqrryptjhw5\nEhsbK4S47777IiMjna/Kmc5lCQkJ06ZNa968eVhYmNForFatWrdu3RYuXHjr1i0Hu86l3e7M\nPqxateqDDz4ohDh79uz27dsdvLWrOBULAIAQQhw9enTYsGE2uSQpKenAgQMHDhxYvHjxl19+\n2bNnT5utrl69OnDgwGPHjlm/mJKScvz48ePHjy9btmzmzJn//e9/vVihECI1NXXs2LE2t2Ge\nPXv27Nmzq1ateu655woPjzmQlpY2evTobdu2Wb+YmJiYmJh4+PDhd955580335wxY4bNViaT\nSQhhsViysrIGDx6szOFy584dpc0HH3wgL0yePNn5epzsPDc3d/r06StWrLDe8OrVq1evXo2N\njX3rrbdWrlw5fPjwwp0Xe7c7NmXKlPXr1wshVq5cOXjwYFc3L4pkUec6p4KCgoKCAp1Op9P5\n/KBgUlKSO5s/+/MRT1ViQ6/TCUkym80q9a8enU6yWIRKx54QYmG7tmp0q9PpwsPDc3NzU1NT\n1ehfVWFhYSkpKertc5UEBAQEBQWlpaXl5OR4uxbX6PX64OBgX5ylIjQ01N/f382/e+oxGo3K\nmJZn3bx5s0mTJvIHb9269bhx4+rVqxcQEHDx4sWlS5fKA28hISGnT5+uVq2a9YZdu3Y9cOCA\nslWDBg0MBsONGzf279+/bt269PR0IcR777331FNPOS7g1q1bycnJmZmZzZs3F0JUq1Zt3759\n8qoqVaoEBQUVu0Kz2dy7d2+5t9q1a48fP75BgwZpaWnx8fFr166VTxcuWLBg/vz5Qojbt2/f\nvn175cqV8kRrs2fPnjJlihAiKCioSpUqcm89evSQP3LVqlWnTZvWsWPHkJCQa9eubdu2bdWq\nVXl5eUKIZcuWPfnkk9Zl9O/f/7vvvhNCrF69esKECUajsW3btiaTqV+/frNmzRJC5OfnV6pU\n6fbt2yaTKSkpKSgoSNn2rlXdtXMhxMiRIzdt2iSEqFy58tSpU1u3bl2xYsXLly9v3bp1zZo1\nZrNZr9d//fXXgwYNcvPAuGu1soKCgmrVql2/ft1gMCQmJnrqkkoXRuzkIcTVq1dbl1WU119/\n/cUXX+zXr9+OHTuKXx0AACVi+fLl8pd3t27ddu3aZTQalVXjx49/8MEHY2Ji0tLSFi9e/Pbb\nbyurEhIS5IjTsmXLgwcPWm/10EMPPfXUU+3bt09LS3v99denTp0qSZKDAsqVK1euXDk5CAoh\n/Pz8IiIi3K9QCPHhhx/Kqa5Dhw67d+9WAtNjjz32yCOP3Hvvvfn5+a+++ur48eNr1aoVHh4e\nHh5erlw5pSqbMt5//335Izdq1Oinn36qUKGC/HrLli379+/ft2/fIUOGCCGeffbZYcOGVa5c\nWdnQz+//IseKFSvatGnzzTff2MSJ+Pj427dvCyG6du1qneqEEHet6q6dr1+/Xk51zZs337Nn\nj9JVq1atBg8ePGzYsPvvv99sNj/++OM9e/YMDg52Z7fftVqZTqe777771q5dm5ubu2fPngce\neKBwm2JwYTjt+++///777zMyMpxpXKNGDSFEQkJCMesCAKAEBQQE9O3bt0WLFrNnz7b+8hZC\nSJKkjPrs2bPHetXp06flhX79+tlsJYRo3LjxkiVL/vOf/7z++uvujzoXr0IhhPKQgw8//NAm\nMPXo0WPMmDFCiPz8fPleVMcsFst7770nLy9dulRJdYr7779/6NChQoiMjAybDpUzeMeOHYuJ\niSk8SBQXFycvdOjQ4a6V2Lhr5/K5ZkmSPvvsMyVyKQYMGDBu3DghxNWrV2NiYqxXFXu3O6N9\n+/bywuHDh4uxuV1qXWP3559/CiEcX4oIAEAp8eyzzz777LNFrW3cuLG8cPXqVevXAwMD5YUT\nJ07Y3VB+fIJHFK/ChISE8+fPCyEiIyOjoqIKbzh79uzu3buXL1++fv36d63h+PHjFy5cEELU\nrFnznnvusdtm1KhRX3/9tRDi22+/tVvwoEGDatWqVfh1ZTCoRYsWd62kKHY7P3PmjBzBO3Xq\n1KRJE7sbPvLII5988okQYtu2bePHj1deL95ud5LySY8fP16Mze26S7ArPLnzypUrC0dda/n5\n+WfPnt24caMQwmZeQQAAfEVeXl5mZqZ8Zaoy3padnW3dpnPnzgEBAVlZWdu2bRszZsyzzz7b\nrFmzUlXh0aNH5YXWrVvb7aRp06ZNmzZ18h2V3tq3b1/UmeU2bdrIC7/99pvFYincrGvXrnY3\nvHjxorxQu3ZtJ+spzG7nBw8elBfsRluZsn/uerLRmd3upDp16sgL8oTMHnGXYDd37lybV1x6\nbG3nzp1drggAAC/58ccf169fHx8ff/369du3b9/1fqPw8PBly5ZNmjSpoKBgw4YNGzZsaNiw\nYc+ePXv27HnPPfeUL1/e6xUqaalq1aruv/vff//fxPVKIilMGTBLTU1NS0srU6aMTQPrC++s\nXbt2TV5wp1S7nSvDaStWrLC5K7Yw5TNac3W3O6ly5cp6vd5sNhdvwM+uuwS7KVOmxMfHnzx5\nshhTAjZu3Fh54B0AAKVZenr62LFj5XOILpkwYUKtWrVefPHFQ4cOCSHOnDlz5syZFStW6HS6\nbt26Pf744yNGjPDIBBHFqzAtLU1esLm6rniUG72tby+wodPp5FFMIURqamrhYFf4yjyZcuOI\nO6Xa7Tw5Odn5HnJzc3Nzc+XZg4UbB4YzJEkKCAhIT0/PzMz0VJ93CXZysM3MzPzll1/kqfZm\nz57t+FSsECIsLCwiIqJnz57enTgbAAAnPfroo/KXd0hIyOzZswcOHFitWrXw8HB/f38hRHZ2\ndkBAQFHb3nPPPffcc8/PP//8zTff7Ny589ixY/KcX/v27du3b99777339ddfV6xY0SsVKpmy\neOcKi0cZ0LJ7ulbJTDaU05qFb0Nxnt3OlZ0wbtw46+vnimKdXtw5MJxhMpnS09MLCgry8vLk\nPt3k1M0TgYGByknrKVOm2L1rFwAAH3Xy5MkvvvhCCBEYGHjw4MHCV2I5M2Nou3bt2rVr9+qr\nr96+fXvv3r1fffVVTExMXl7eoUOHHnroIWXK3BKuULnY/a7PpXCGMteag+k8zWazEiJdutRe\nyXM5OTlFhb/iUcooV65cjx49nN/QIweGY/K+0ul0Hkl1wqXpTubPnz9//vzw8HCPvDEAAKXE\n999/Ly+MHDnS7vX18q2gTgoPDx8+fPhnn3127NixSpUqCSH27dv3008/eaXCunXryguJiYnu\nFCCrWbOmvHDu3Lmi2iiVlC1b1sEZ28KUM7BOTqzmPGUnnD171qUNPXtgFCY/KkNY3V7tPheC\n3YIFCxYsWECwAwBojHLZvjJ7hQ3rp7U6r2nTptHR0fKymxO7FrvCVq1ayQtxcXF2L/k/ffr0\npEmTJk2atGTJkruW0bbt/z3XJz4+vqhH18fHx9s0dpIy+ZwH7ySQtWvXTl6IjY3Nzc11fkOV\nDgzF9evX5TE/Zx794CSff94XAABuUk4Cyk8+sHH16tV3331XXra+lbCgoGDevHl9+vR5+OGH\ni+pZGYVy80qs4lUohGjSpEnDhg2FEImJid98803hbdevX79q1apVq1bZPVdr01tUVJR8OdbV\nq1eV0Swbn376qbwwbNgwRx+pEGWWE+VO3qK4ekNnRESEPGPcnTt3lPJs7Nu3r379+jNmzLCe\nkrDYu93JapUBP3dmeLFRnAmK79y5c/z48Rs3bijzuDjgzFWKAAB4kXKWbevWrS+//LLyfCoh\nxOXLlwcMGFCzZk2dTpeUlJSRkZGcnFy2bFkhhE6nO3DgQGxsrBCib9++Y8eOtek2MzNTefpC\nx44drVc9/fTT8r0Cs2fPduZLvXgVymbNmiU/q3Tq1KktWrSwnr/36NGjcjTx8/OznktZuZbO\n5tyl/KwF+SGw06ZNO3TokM1dqKtWrdq9e7cQolKlSqNHj77r57KmTAF4/Phxu6GwqKqcMXv2\nbPkZG88880zr1q1tZvW7cOHCo48+ev78+SVLllj/HN3Z7c5Uq8xL7MHpD10LdpcuXZoxY8a2\nbducv1qQYAcAKOUGDhwYHh5++/btU6dO9enTZ/bs2TVr1kxMTNy5c+eKFStyc3N//vnn6Oho\n+Rmpc+fOjY6OLlu2bPXq1V977bWePXuazeZx48Zt2LDh/vvvr1GjRnBw8J07d44dO7Zu3Tp5\n8GnEiBE2DzxYuXKlfCXZmDFjnAl2xa5QCDFp0qSNGzf++OOPly9fbtGixYQJE5o3b56VlRUf\nH79hw4a8vDwhxPPPP1+vXj3l7ZS7JDdu3FijRo0GDRpcvnx5zpw5Op1uypQpmzdv3rNnz19/\n/dWqVatZs2a1b9/eZDJdunQpJibm888/F0Lo9fpPP/3UpQvshFX2Ler5Wg6qumvno0eP3rJl\nS0xMTGpqaufOnSdPntynT5+yZctev349Njb2k08+keeFeeyxx5ST127udmeqVU5bF+MpakWR\nnJ9k78aNG61atbpy5YpLb+CpSfy8SH76b7E9+/MRT1ViQ6/TCUly/5ackqfTSRaLisfGwnau\nXdjhJJ1OFx4enpub6+B2sFIrLCwsJSXF534fAwICgoKC0tLS3H/OZgnT6/XBwcHKpF8+JDQ0\n1N/f382/e+oxGo0hISFq9PzNN9+MGDGi8AVYoaGhW7du7d69+7Jly6ZOnaq8/txzz8kPZ9q4\ncePkyZOVOdgKe+CBB9auXWtzdXxwcLAc7OLi4qy/1NPT0+UPWKtWLZszksWuUO529OjRdk/F\nSpI0Z84c+VGqCrPZHBUVpTwJV5aXlycPWWVkZIwbN27z5s12P294ePjatWsHDBhg8/qQIUO2\nbt0qhIiNje3SpUvhDfPy8ipVqpScnBwQEJCUlFT4fgIHVd21c7lldHT0xx9/bPcvoU6ne+qp\npxYtWmQzU1uxd7vjfSiEsFgs1apVu3btmr+//40bN5QRPje5MGL3zjvvKKkuKioqMjIyNDSU\nmeoAABowePDgw4cPv/322/v3779x44bBYKhfv/7w4cOnTJkin22cMmXKlStX1q9ff+PGjZo1\naypP+Rw5cmTPnj0/+eST3bt3nzlzJikpKT8/PyQkpFatWh06dBgzZkxROaPEKhRCBAcHb926\ndefOnevXr4+Li0tMTCwoKKhWrVrPnj2jo6ObN29u8156vX7nzp0zZsw4cOBAampq+fLlo6Ki\nlKGmoKCgmJiYn376ac2aNQcOHLh69Wpubm54eHhkZGS/fv0mTZpUeFJiZ/j7+99///2ffvpp\nVlbWd99998ADD7hUlTP9f/jhh08++eQnn3yyb9++f/75Jz09PTg4uG7dut26dXv00UcjIyML\nb1Xs3X7Xag8dOiTfnNG7d29PpTrh0ohdZGTk77//HhISsm3btu7du3uqgtKPETuPY8Su5DFi\nV8IYsVOJeiN2KA3i4+Pl8cs+ffrs3LnT2+Woa9y4cfIlmFu2bLn//vs91a0Ld8XKY8JTp079\nV6U6AABQMtq3by8PcO7atevUqVPeLkdF165d27hxoxAiIiJi0KBBHuzZhWAn/4/Z5kYSAAAA\nT1m4cKEQwmKxPP/8896uRUXz58+Xr9t78803PfIoYYULfcnPubO+1xcAAMCDOnbs+Mgjjwgh\ntmzZsnfvXm+Xo4rffvvtk08+EULcc889hS8ldJMLwe6ee+4RQpw5c8azFQAAACjee+89+dll\nEydO9MVrmh3LyckZO3as2WwOCwsrarZkd7gQ7GbMmKHT6T7++GOfu4oZAAD4irCwsI0bNxqN\nxkuXLk2ePNnb5XjY7NmzT5w4IUnS6tWra9So4fH+XQh2rVu3XrJkydmzZx966CHtJWgAAFBK\ndOzYUT5Z+cUXX7zyyiveLsdjPvroo6VLlwoh3nrrrSFDhqjxFi5Md2I2m7OysjZv3jx9+nSD\nwTBmzJgOHTpUrFjR8VV3npq/x4uY7sTjmO6k5DHdSQljuhOVMN0J4JgLd0LYBDjlwbeO+dwX\nCQAAgI/y5B22AAAA8CIXRuy6d+9uMpn8/Pz0er0kSerVBAAAgGJwIdjt27dPtTIAAADgLk7F\nAgAAaATBDgAAQCMIdgAAABrhwjV2hw8fdqnrnJycjIyM/v37u1gSAAAAisOFYNexY8divAHz\n2AEAAJQMTsUCAABohAsjdgMGDHCwNj8//8aNGydPnszLyytTpszYsWODgoJ48AsAAECJcSHY\nbd++/a5t0tLSPvroo//85z9Hjhz5+uuvq1Sp4kZtAAAAcIGHT8WGhITMmjXr+++//+WXX/r2\n7ZuRkeHZ/gEAAFAUF0bsnNe5c+fRo0evWbNm1apV06ZNU+MtAADwiLS0NDW65WIkeIUqwU4I\n0bdv3zVr1qxZs4ZgBwAo5fx2bPFsh/n9h3i2Q8BJat0VGxoaKoQ4c+aMSv0DAADAhlrB7sqV\nK0KI3NxclfoHAACADVWCndls/vTTT4UQ5cqVU6N/AAAAFObCNXaXL1923MBsNqempp48efKD\nDz44ePCgEKJNmzZuVQcAwL+M2Wz+7LPP1q5d+9tvv6WkpISHh3fo0OHJJ5+87777vF0afIAL\nwa5GjRqu9v7kk0+6ugkAAP9aOTk5DzzwwLfffiuECAwMrFy58o0bN7Zu3bp169ann376nXfe\n8XaBKO3UusZOp9O9+uqr/fr1U6l/AAC0Z/78+d9++21AQMDatWvv3Lnz999/JycnL1y4UJKk\nRYsWbdy40dsForRzYcSuadOmjhtIkmQymSpWrNiyZcuHH364SZMm7tUGAMC/yK1bt959910h\nxDvvvPPII4/ILwYEBDzzzDOXLl1atmzZ888//9BDD0mS5NUyUaq5EOxOnjypXh0AAPzLffnl\nl7m5uaGhoZMmTbJZNWPGjGXLlp0/f/7gwYNdunTxSnnwCWqdigUAAC45dOiQEKJr164Gg8Fm\nVURERPXq1ZU2QFHUevKEqqZNm3bx4sWi1vbo0WPWrFnWrxQUFOzfv3/v3r0XLlzIyMgICQlp\n2LBh//79W7ZsqXqtAAA4Rz4z1rBhQ7trGzRocPny5YSEhJItCj7GrWBnsVjS0tJSU1OFEGFh\nYcHBwR6q6i4yMjKEEEajUa/XF15rNBqt/5mXl/fGG28cPXpUXlW2bNmUlJT4+Pj4+PghQ4ZM\nnDixZGoGAMCxW7duCSEqVapkd23lypWVNkBRihPsrl+/vmbNmh07dvz2229yqpOFh4e3adNm\n2LBhY8aMCQoK8lyRttLT04UQzzzzTLt27e7a+LPPPjt69KjBYIiOju7WrZter8/Nzd2+ffua\nNWu2bNlSv379rl27qlcqAABOSktLE0IEBATYXSu/bv21CxTm8jV2y5cvj4iImDNnzk8//WRz\neN2+fXvXrl2PP/54RETEzp07PVfk/6OgoCArK0sI4Ux2TEtL27p1qxBi4sSJPXv2lEf4DAbD\nsGHD+vfvL4RYt26dxWJRqVQAADxF/rbillg45lqwW7x4cXR0tHwmVBEQEGDz34vr168PHDhw\nx44dHiiwEHm4TgjhzJnfAwcO5OfnBwYGFp6we/DgwUKI69evnz592uNFAgDgqjJlygghMjMz\n7a6VX5fbAEVxIdj9/fffc+bMkZeHDh36xRdfnD9/3mw2Z2ZmZmZm5ufnnz17dv369b179xZC\nmM3msWPHyqPKnqXESmdG7P744w8hRNOmTf38bE86V6lSpXz58kobAAC8q0KFCkKI69ev2117\n9epVUfQVeIDMhWvsVq5cmZOT4+/vHxMTIw93WdPr9REREREREaNHj161atXkyZNv3br10Ucf\n2dyg6j5lxC4/P3/Tpk3Hjx9PTk42GAzVq1fv0qVLhw4drIepL126JISoVq2a3a6qVq2alJTk\n4AZbAABKTLNmzY4ePWr3PJLFYpFfb9WqVYnXBV/iwojd3r17hRCTJk0qnOpsPProoyNHjhRC\nqHGlnRLsZsyYsWHDhpMnT165cuXChQuxsbFvvPHGCy+8oDQQ/7sQNSwszG5XZcuWFVyICgAo\nHbp16yaEiI2NlS8lt/brr7/evHlTCNGjR4+SLww+xIURu/PnzwshBg0a5Ezj4cOHf/7557//\n/nsx6yqaktvKlSsXHR3drFmzoKCga9eubd68ee/evSdOnFi0aNH8+fPlNvLvhs0EKAp5BsjC\nVzOsXbtWuaOiYcOGUVFR7hSs06l2oaukcv+qkYQkScKiWuFF3VPmJnkwWK/Xq9S/qnQ6XUBA\ngM/dKuTv7y+EMBgMOp2Pzaau0+nkfe7tQlwm7+pSW7nPHQkueeCBB6ZOnZqenv7BBx/YnO96\n6623hBBt2rRx8ysJmudCsEtOThZCVKlSxZnGtWrVEupMt9OoUaN58+bpdLoWLVooc3PXqFFj\nxowZ4eHhMTExv/zyy4kTJ5w59Iu6w2j58uX5+fny8ogRIzp06OBOwZKk7p8htftXjyTUSnaq\nzraj1+tV7V89gYGB3i6hmIxGY1H/PSvlfPRQEaW4cuWPsyYFBwc///zzc+fOnTdvXtmyZceM\nGePv75+amvrKK698+eWXQoh33nnH2zWitHMh2AUEBOTl5Tl5P0R2drb435CYZ1WoUEG+vLSw\nkSNH7ty5Mz09/fDhw3KwCwwMTE9Pz8nJsdtefr3wt917772nLFesWDElV2EUAQAAIABJREFU\nJcWdggsKzO5s7oBO0glJUq9/9UiSTgiLeqNHbv7IiiJJUpkyZfLy8oq6Z600Cw4OzsjI8LkR\nO6PRaDKZMjMz8/LyvF2La/R6vclksplDwCcEBwfr9XqVfonc5+fnV/hmOC155plnfv/99/Xr\n10+cOHHq1KnlypW7fv16Xl6eJEmLFy/u3r27twtEaefCr0eVKlVSU1Pj4uKcmdE3Li5OCFG1\natXil+Y6g8FQu3btkydPyhciCCHKlClz48YNeayxsNu3bwt7V+DZzHuclJTkTlUqfpNKKvev\nGkmyWCwqVq5SCJDPAVksFp8LGeJ/ZftcsJO/ws1ms8/t84KCAqPR6HNlCyEKCgr0en2prVzb\np2KFEHq9ft26dYMHD/74449/+eWX69evV6xYsWvXrrNmzWrbtq23q4MPcCHYdenS5cyZM0uW\nLJkwYUJRY2ayGzduLF68WN7E3QJdJI/SK/+fq1279l9//fXPP/8UbmmxWC5fviyEqFevXklW\nCACAYyNGjBgxYoS3q4BPcuG/PqNHjxZCXL16tVu3bnv27LHbpqCgYMeOHZ07d75y5YoQYuzY\nsR6p0lpcXFxMTEx8fHzhVbm5ufLcJcr8JpGRkUKIU6dO5ebm2jQ+d+6cfK6BC1EBAIA2uDBi\n17Nnz0GDBm3btu2PP/7o3bt3rVq12rVrV6dOneDgYIvFkpaWdu7cucOHD1+7dk1uP3z4cPnO\nbc+Ki4vbt29fhQoVoqKibC6P+/LLL+Vr+9q3by+/0qlTpxUrVmRnZ+/YsWPIkCHWjTdv3iyE\niIiIkO/zAAAA8HWuXYK6YcOG/v37HzhwQAhx6dIlefpfu+699941a9a4W509AwcO3L9//82b\nNxcsWDBlyhT5RGpWVtaOHTtiYmKEEF27do2IiJAbm0ymBx98cO3atevWrQsODpYfF5uZmblp\n06aDBw8KISZOnKhGkQAAACVPcvViarPZ/P777y9ZsqSoBzY0aNBg5syZU6ZMUe9Bxd9///2K\nFSvMZrMQIiQkxGg0Jicny/9s06bNs88+azKZlMYFBQWLFy/et2+fEMJoNIaEhMiNJUmaNGmS\nM9PyuXnzxLM/H3Fncwf0Op2QJPmD+xadTrJYhHoX8i9sp8olxjqdLjw8PDc31xcntQ4LC0tJ\nSfG5mycCAgKCgoLS0tKKure91NLr9cHBwaX23lIHQkND/f393fy7px75z7hn+1Tj6ZdCCI/X\nCTjD5WAns1gsx48fP3r06N9//52SkiJJUmhoaM2aNdu1axcZGalepFP8888/27dvT0hISEpK\nMpvNZcqUadCgQa9evdq1a2f33Q8ePLhr166//vorMzMzLCysSZMmQ4YMqV+/vjPvRbDzOIJd\nySPYlTCCnUoIdoBjxZwNSJKkFi1atGjRwrPVOK9GjRpPPPGE8+07d+7cuXNn9eoBAPiu6X9f\n8WyHS2raf0Y5oDaNTwgEAADw71GcYHfp0qVXXnnlzz//LLxqyZIlL7zwgvxUWQAAAJQk14Kd\nxWJZsGBBRETEf/7zn7NnzxZucOLEiddee61Ro0YvvfSShyoEAACAU1y7xm7OnDkLFy6Ulx1c\nWpuXl7dgwYKcnJzXX3/dreoAAADgNBdG7I4dO/b2228LIfz8/MaPH9+mTZvCbZ5++ul58+YF\nBAQIId58882EhARPFQoAwL/H5cuX+/TpI0mSJEl37tzxdjnwGS4Eu+XLl1ssFj8/vx9++GH1\n6tVNmzYt3KZx48avvfbajz/+6OfnZ7FYli5d6rlSAQD4V1i9enVkZOSuXbu8XQh8jwvBTp7j\nd+zYsT169HDcsn379g8//LCyCQAAcMa1a9cGDBgwceJESZJ4NhKKwYVgd+XKFSFEhw4dnGks\nN5M3AQAAzti0adOOHTt69uyZkJAwdOhQb5cD3+NCsNPpdMLpqbQDAwOVTQAAgDNMJtPbb7+9\nZ8+eGjVqeLsW+CQX7oqtWrXq2bNn7U5fV9hvv/0mhKhUqVIx6wIA4N/nscceY0wE7nDh6Ona\ntasQYvXq1RkZGY5bXrp06dNPPxVCdOzY0Y3aAAD4dyHVwU0uHEBjxowRQly8ePHee+89efKk\n3TYWi2Xr1q1dunSR782WNwEAAEAJcOFUbM+ePUePHr1hw4a4uLioqKhmzZq1bNmyatWqQUFB\n2dnZN2/eTExMjIuLS0xMlNsPHjy4T58+6pQNAAAAW649eWL58uWXL1/ev3+/ECIhIcHB/MM9\ne/bcsGGDu9UBAADAaa6dyy9TpsyePXuWLl1at27doto0bNhw5cqVu3fvDg4Odrs8AAAAOMu1\nETshhF6vj46Ojo6OTkhIOHr06MWLF9PS0nQ6XWhoaN26dVu1atWkSRM1CgUAAIBjLgc7RbNm\nzZo1a+bBUgAAAOAObqsGAADQCIIdAACARhDsAAAANKL419gBAADPqly5cnZ2trycn58vL9Sq\nVUuSJHl55syZ8+fP905x8AUEOwAASos7d+7k5OTYvJiamqosZ2VllWxF8DEEOwAASgtluA4o\nHq6xAwAA0AiCHQAAgEYQ7AAAADSCa+wAAP92S2pW83YJgGcQ7AAA/2ohISHeLgHwGE7FAgAA\naATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATB\nDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAA\nQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMI\ndgAAABpBsAMAANAIgh0AAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQCA/6+9Ow+Pqj77P36f\nM8lMyEIWFlnEUCBACRERECSECioopRhTropLCYJLBUH0gfbC2oKXEYtoEbVXUZSC22VlKZRF\n1AiRgKIERUAFy5KwBITgkIUwmUxmfn+cX+fJkw1iOJlzvrxff3gdz/nOd+7cTJJPzgpFEOwA\nAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFBEW6gJswOFwNOXl2qWqo775NZGAye9hAlPL\nbuI/WX10XRcRTdNMmt9URtmBgM0+K5qmiYiu67bruVGw7cqW//bcspUb5QGoD8HuwqKjo5vy\nciMNmML4nafppofHS04TEdF0s+pu4j9ZwxwOh6nzm0TX9aioqFBX0WjGt09ERITT6Qx1LY1j\nJGk7flSMSGfZyv1+f6hLACyNYHdhxcXFTXl5lWk/hhy6Lppm3vzm0XUtEBDz9h418Z+sPrqu\nJyQk+Hy+kpISM+Y3VVxcXElJie322LVo0SIqKqq8vLyioiLUtTSOkepM+iiaKjY2Njw83LKV\nu1wul8sV6ioA6+IcOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQ\nBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4A\nAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEE\nOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAA\nRRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwA\nAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRB\nsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAA\nUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUERbqAn4in8+X\nnZ2dm5ubn59fXl4eGRmZmJiYmpo6YsSI8PDw6iOnTZuWn59f3zw33HDDY489Znq5AAAA5rNl\nsHO73bNnzzbimqZpLVu2LCkp2bt37969ezdu3JiVlRUbGxscfO7cORFxuVwOh6P2VC6Xq7mq\nBgAAMJf9gl0gEJg7d25+fn5ERMSkSZOGDRvmdDo9Hs+GDRuWLVtWUFCwePHiGTNmBMeXlZWJ\nyMyZM6+77rrQVQ0AAGA6+51jt3v37v3794vI1KlTR44c6XQ6RSQiIiIjI2P06NEi8umnn3o8\nHmOw3+8/f/68iERFRYWuZAAAgOZgv2BXVlaWnJzctWvXwYMH19jUr18/EfH5fKdOnQoONhai\no6Obs0gAAIDmZ79DsampqampqXVu0jTNWDB248l/T7AT9tgBAIDLgP2CXQPy8vJEpH379u3a\ntTPWBPfY+Xy+f/7zn19//bXb7XY6nVdeeeWQIUMGDRoUzIIAAAB2p06wO3jw4Pvvvy8imZmZ\nwZXBYDd9+vTy8vLg+sOHD+fm5qakpMyaNYujtAAAQA2KBLv8/Pw5c+b4fL6bb765+rl3wWDX\nqlWrKVOmXH311VFRUSdOnFi5cuWmTZv27Nnz/PPPz549u8Zsjz/+uN/vN5YHDRp06623NqU2\nXTftREbN5PlNo2miiQRM210aExNjxrTG/t2wsDCT5jeVw+GIiYkJBAKhLqRxjLsURUREBM+v\nsAtN04yeh7qQRjN6bsfKAYgawW7Hjh3z58/3eDxpaWlTpkypvqlnz56PP/64ruvXXHNN8BdD\np06dpk+fnpCQsGLFip07d+7ZsyclJaX6qzZt2uTz+Yzl+Pj49PT0ppRn9tFeux5N1sS8uk29\nPaGu6za9/aHtslFQjbuO24hNPypi4cqDP5wB1Mn2wW7lypVvvPFGIBC4/fbbJ0yYUCPltGnT\npk2bNnW+cNy4cRs3biwrK9u+fXuNYLdq1argjo2oqCi3292UCv1VVU15eQN0XRdN8/urxGZ7\nYUTTNREJ+M2qu4n/ZPXRdT02NraysjK4J9hGWrZsWVpaars9di6XKzIy8ty5c16vN9S1NI6u\n61FRUaWlpaEupNFiYmLCwsJM+iZquvDwcM6fARpg42Dn9XoXLlyYm5vrdDonT548fPjwRr3c\n6XR27tx57969p0+frrGpQ4cO1f+3qKioKXWa/YvUbr+pRUQ0kUDAxM5UmROmjVQUCARMmt9U\nRtm2C3ZGwX6/3749D3UVjWb03LKVh4XZ+NcW0Azs+h3i9XqzsrJ27doVHx//xBNPJCUl/YRJ\njF36/JgAAABqsGWm8fl8c+fO3bVrV8eOHbOyslq1alXfyM8+++z48eOdOnUaOHBgjU1er9d4\n2mzHjh1NrRYAAKB52DLYLV269Msvv2zbtu3TTz+dkJDQwMjPPvssJyenTZs2KSkpkZGR1Tct\nX77cePJY7cwHAABgR/a7U8ahQ4fWrl0rIpMnT2441YnI6NGjNU07ffr0nDlzDh48aKw8f/78\nypUrV6xYISJpaWndunUzu2YAAIBmYL89duvWrTPO7X322WfrGzN27NixY8eKSPfu3SdPnrxo\n0aJ9+/Y9+uijMTExLpfL7XYb5wX3799/6tSpzVY5AACAqewX7CoqKoyF6k+SqKGysjK4PHLk\nyF69eq1bt2737t1FRUXnz5+PjY3t3r37jTfeeN1119n1JnAAAAC12C/YzZw5c+bMmY16SadO\nnR566CGT6gEAALAI+51jBwAAgDoR7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbAD\nAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAE\nwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARYSFugDg0ov4cJ0Z02qa5nO5\nxO+P8HrNmF9EPCNGmzQzAOBywB47AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAA\nABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGw\nAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQ\nBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4A\nAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEE\nOwAAAEUQ7AAAABQRFuoCgEtvWtwVZkyriegOhwQCVX6/GfOLyLMmzQsAuDywxw4AAEAR7LG7\nsPDw8Ka8XNMuVSGhmd8cmqYFQl3DT2dez5v4YWuApmnh4eGBgM3a7nA4jP+a1xmT6Lpu9DzU\nhTSaruti5kexiYzyANSHYHdhLperKS/XNNN+DGkmz28ao3CbZlLRRDNtV3cTP2wN0HXd5XLZ\nNNiFh4fb7te5pmlGz0NdSKNpmiZmfhQBmIpgd2FlZWVNebnftPOxHLoumonzm0fXtUBAbBcy\nNBHN4ZCAiT1v4oetAWFhYWVlZbbreYsWLcLDwz0eT0VFRahraRyHwxEdHW3eP6h5YmNjdV23\nbOUulysiIiLUVQDWZbM/ggEAAFAfgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2\nAAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACK\nINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEA\nACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJg\nBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACg\nCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0A\nAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoIiwUBcA4H/9\nT+EPJs3sLHJ7vV6TJheR5ztcYd7kAICLxB47AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAA\nAEUQ7AAAABRBsAMAAFAEwQ4AAEARl8UNiv1+/yeffLJp06bDhw+fO3cuJiamR48eo0aN6tu3\nb6hLAwAAuGTUD3aVlZXPPPNMXl6eiLhcrvj4+OLi4s8///zzzz9PT0+fOHFiqAsEAAC4NNQP\ndu+8805eXp7T6ZwyZcrQoUMdDofX6123bt2yZctWr16dlJSUlpYW6hoBAAAuAcWDXWlp6Zo1\na0Rk4sSJw4YNM1Y6nc6MjIzTp0+vX7/+zTffHDJkiKZpIS0TsD2TnnIbFhYWFhZWWVlZVVVl\nxvz2fcStqY8V1nXd4/GYNL99ew7YguIXT2zdutXn80VGRo4YMaLGpjFjxojIyZMnv/vuu1CU\nBgAAcIkpHuz27dsnIsnJyWFhNfdNtm/fvnXr1sExAAAAdqd4sCsoKBCRjh071rm1Q4cOIpKf\nn9+cJQEAAJhE8WBXWloqInFxcXVujY+PF5GSkpJmrQkAAMAcil88cf78eRFxuVx1bnU6nSJS\nXl5eY/2ECROCZ2rfeOON99xzT1NqcOimpWdNM3d+82iiaSIBe16zomnm9dzhdJo0s67rTtMm\nN49xYVNYWJjD4TBj/vr+6ms6TdN0XTdvfmeR26SZdV2X//54NEMTe+L3+y9VJYCSFA92DQsE\nAvLf3xzV7du3z+fzGcu9e/eufX5eoyy+ZWRTXg7Avpr406MBi3smmTSzxQV/OAOok+LBLjIy\nsqysrKKios6txvrIyMga67dv3179f4uKikwqr4liY2PDw8PPnDljJFQbiYyM9Pv95t1PwSS6\nrickJHi9Xjsevo+LiysuLrbdR6VFixZRUVGlpaX1fRdblsPhiI6OLi4uDnUhjWb8YLHszz2X\nyxUTExPqKgDrsuFRvMZo2bKliLjddR+z+PHHH8XMYzEAAADNSfFg17lzZxE5evRo7U2BQODY\nsWMi0rVr12auCgAAwAyKB7vevXuLyLfffuv1emtsOnjwoHGUJCUlJQSVAQAAXGqKB7vBgwdH\nRER4PJ4NGzbU2LRy5UoR6datW2JiYihKAwAAuMQUD3YRERG/+c1vROTNN9/Mzs42bmJSXl7+\nj3/8Y9u2bSIyceLEEJcIAABwiSh+VayIZGRkHDlyJCcn58UXX3zllVdiYmLcbndVVZWmaffd\nd59xrBYAAEAB6gc7Xdcfe+yxgQMHfvjhhwcOHHC73XFxcb169UpPT09KukxvBAUAAJSkfrAz\npKampqamhroKAAAAEyl+jh0AAMDlg2AHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiC\nHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACA\nIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAILRAIhLoG/ERv\nvfXWkSNHfv/734eFhYW6lsvCuXPnFi5c2L1797Fjx4a6lsvFjh07Pvroo/T09F69eoW6lsvF\nG2+8cezYsVmzZmmaFupaADQae+xsLDc3d9WqVX6/P9SFXC48Hs+qVau2b98e6kIuIwcOHFi1\natXRo0dDXchlJCcnZ9WqVfzND9gUwQ4AAEARBDsAAABFEOwAAAAUwcUTAAAAimCPHQAAgCII\ndgAAAIog2AEAACiCG9vaj9/v/+STTzZt2nT48OFz587FxMT06NFj1KhRffv2DXVpoeTz+bKz\ns3Nzc/Pz88vLyyMjIxMTE1NTU0eMGBEeHl5jcKN6aJHB1rdt27Z58+aJyPjx42vfw9kibVSg\n52VlZatXr96+ffupU6ccDkfbtm1TU1NvvfXWmJiYGiMt0kYFeg7YCBdP2ExlZeUzzzyTl5cn\nIi6XKyYmpri4uLKyUkTS09MnTpwY6gJDw+12z549Oz8/X0Q0TWvZsmVJSYnx2U5MTMzKyoqN\njQ0OblQPLTLY+txu98MPP1xaWip1BTuLtFGBnhcUFPz5z392u90iEhcX5/P5ysrKRKR169bz\n5s1r06ZNcKRF2qhAzwF7YY+dzbzzzjt5eXlOp3PKlClDhw51OBxer3fdunXLli1bvXp1UlJS\nWlpaqGtsboFAYO7cufn5+REREZMmTRo2bJjT6fR4PBs2bFi2bFlBQcHixYtnzJgRHN+oHlpk\nsPW9/PLLpaWlLperoqKi9laLtNHuPS8vL58zZ47b7U5JSZk8eXLHjh1F5JtvvnnuueeKior+\n9re/zZkzJzjYIm20e88B2+EcOzspLS1ds2aNiEycOHHYsGEOh0NEnE5nRkbGqFGjROTNN9+8\nDHfB7t69e//+/SIyderUkSNHOp1OEYmIiMjIyBg9erSIfPrppx6PxxjcqB5aZLD1ffDBBzt2\n7OjZs2fPnj1rb7VIGxXo+fLly8+cOdOpU6fZs2cbqU5EkpOTp02b1r1794SEBK/Xa6y0SBsV\n6DlgOwQ7O9m6davP54uMjBwxYkSNTWPGjBGRkydPfvfdd6EoLZTKysqSk5O7du06ePDgGpv6\n9esnIj6f79SpU8aaRvXQIoMt7ocffnj99dfDwsImT55c5wCLtNHuPff7/dnZ2SIybtw446+X\noL59+z733HPTpk0LrrdIG+3ec8COCHZ2sm/fPhFJTk4OC6t5DL19+/atW7cOjrmspKamPvPM\nMwsWLDD2B1SnaZqxEPyF16geWmSwlQUCgRdeeMHj8dx1112dO3euc4xF2mj3nn///ffFxcUO\nh2PAgAEXHGyRNtq954AdcY6dnRQUFIhI8BBMDR06dCgqKjIuIIDBOGW7ffv27dq1M9Y0qocW\nGWxl//rXv7755psePXpkZGTUN8YibbR7zw8fPiwiHTp0iIiIKCws3LRpU35+fkVFRdu2bQcO\nHDhgwIDgnzFimTbaveeAHRHs7MS45DAuLq7OrfHx8SJSUlLSrDVZ2MGDB99//30RyczMDK5s\nVA8tMtiyCgoK3n77bZfL9eijj+p6vbv/LdJGu/f85MmTIpKQkPDBBx+88sorPp8vuOmjjz5K\nSUmZNWtWdHS0scYibbR7zwE74lCsnZw/f15EXC5XnVuNo43l5eXNWpNV5efnz5kzx+fz3Xzz\nzdXPvWtUDy0y2JqqqqoWLFhQWVk5YcKEDh06NDDSIm20e8+N2o4fP75o0aKbb7755ZdfXrFi\nxZIlS+644w5d1/fs2fPCCy8EB1ukjXbvOWBH7LFTh3FxWfXDMZetHTt2zJ8/3+PxpKWlTZky\n5eJf2KgeWmRwqLz99tuHDh3q06ePcXnjT2aRNlq/58a934qKiu6+++477rjDWNm6deu77767\nZcuWixcv/uKLLw4cONCtW7cLTmWRNlq/54AdscfOTiIjI0WkzvuEBdcbYy5nK1euzMrK8ng8\nt99++4wZM2ocImxUDy0y2IL279+/atWqyMjIadOmXfAXs0XaaPeeG/u9NE371a9+VWPTqFGj\njMp37NhhrLFIG+3ec8COCHZ20rJlSxExbjpf248//ij1n85yOfB6vfPnz1+2bFl4ePj06dPv\nvffe2pmjUT20yGCrqaioWLBggd/vf+CBB6o/6qA+FmmjrXsu/63f5XLVTkIOh8M4Gn769Onq\ng0PeRrv3HLAjDsXaSefOnQ8cOHD06NHamwKBwLFjx0Ska9euzV6XJXi93qysrF27dsXHxz/x\nxBNJSUl1DmtUDy0y2Gq2bdtWWFjocDjWrFlj3H426MSJEyKydu0nw+/mAAAKwklEQVTa3Nxc\nEZk/f77T6bRIG23dcxG56qqrRMTj8RiPQq6x1dgzHfxLxiJttHvPATtij52d9O7dW0S+/fbb\n4P3lgw4ePFhcXCwiKSkpIags1Hw+39y5c3ft2tWxY8e//vWv9aU6aWQPLTLYaozrMauqqg7X\nYjzhw+12G//r9/vFMm20dc9FJDk52chttW/8FggECgsLReSKK64w1likjXbvOWBHBDs7GTx4\ncEREhPEU1BqbVq5cKSLdunVLTEwMRWkhtnTp0i+//LJt27ZPP/10q1atGhjZqB5aZLDVjBgx\n4t/16NOnj4iMHz/e+N+IiAixTBtt3XMRSUhIuPrqq0Xk3XffrfEYro8//risrExErr32WmON\nRdpo954DduSo/tBoWFxYWJimaV9//fXevXtbtWqVmJio63p5eflbb7314YcfisiMGTPatm0b\n6jKb26FDh1588UURmTlzZpcuXRoe3KgeWmSwjWzevPmHH37o06dPr169gist0kYFet6pU6fs\n7OzTp08fPHjwmmuuiYiICAQCOTk5ixcvrqysvPbaa4O3ibZIGxXoOWA7Gg9gthe/3//CCy/k\n5OSIiMvliomJcbvdVVVVmqbdd999tS+Xuxy8+OKLxjM0G7i8buzYsWPHjjWWG9VDiwy2iz/9\n6U9ff/31+PHjg902WKSNCvR88+bNL730ks/nczgcrVq1KisrM+4D16VLlyeffDI2NjY40iJt\nVKDngL2wx85mNE27/vrrr7rqqvLy8uLi4tLS0ri4uP79+0+bNu36668PdXWhsWXLliNHjohI\nZf169eoVPJWnUT20yGC7qHOPnVimjQr0/Gc/+9ngwYMrKytLSkrcbrfD4ejSpUtGRsZDDz0U\nFRVVfaRF2qhAzwF7YY8dAACAIrh4AgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsA\nAABFEOwAAAAUQbADAABQBMEOUNy6deu0i9atW7faMwQCgfXr199zzz1JSUkxMTEul+uKK674\nxS9+8dRTT504caL5vyIAQH3CQl0AAEs7evTonXfeuW3btuorT506derUqS1btsydO3fevHnT\npk0LVXkAgOoIdoDiunfvPnv27IbHnD17duHChSKSmJhYfX1xcfHw4cMPHDggIikpKQ8++GB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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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ZPoK1eueHl5vfbaa5qkAwAkQ7EDAACQBMUOAABAEhQ7AAAASVDsAAAAJEGxAwAA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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# m_near_cps3についても傾向スコアマッチング。Caliper使用\n",
        "m_near_cps3 <- matchit(data = cps3_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  caliper=0.25,\n",
        "  method=\"nearest\")\n",
        "\n",
        "# 調整後データ\n",
        "matched3_data <- match.data(m_near_cps3)\n",
        "\n",
        "# 調整後データでヒストグラム描画\n",
        "\n",
        "# 傾向スコア\n",
        "ggplot(matched3_data, aes(x=ps, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=0.05) +\n",
        "  # scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "# re78\n",
        "ggplot(matched3_data, aes(x=re78, fill=as.factor(treat))) +\n",
        "  geom_histogram(position = \"identity\", alpha=0.6, binwidth=10000) +\n",
        "  # scale_y_log10() +\n",
        "  ggtitle(\"distribution of ps\") +\n",
        "  theme(text=element_text(size=20))\n",
        "\n",
        "\n",
        "# 回帰分析(解析モデルにも共変量を使用)\n",
        "psm3_co_result <- lm(data = matched3_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75) %>%\n",
        "  tidy()\n",
        "\n",
        "print(psm3_co_result)\n",
        "\n",
        "# バランシングの評価\n",
        "love.plot(m_near_cps3, threshold = 0.1, binary=\"std\")\n",
        "\n",
        "# caliperを設定することで改善。基本caliperは設定する方が良い。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PLuXdXbP5Pya",
        "outputId": "b12e89b4-1e77-461f-b845-04fd1dd42184"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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pOTi/XftWtXtZP//Oc/t27dMj96I0aM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\nhGAHABAJwQ4AIBL/F3ba9RNNg85VAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term          estimate std.error statistic p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept) -\u001b[31m\u001b[4m1\u001b[24m71\u001b[39m\u001b[31m6\u001b[39m\u001b[31m.\u001b[39m      \u001b[4m4\u001b[24m417.      -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m389\u001b[39m   0.698 \n",
            "\u001b[90m 2\u001b[39m treat        \u001b[4m1\u001b[24m887.       962.       1.96    0.051\u001b[4m0\u001b[24m\n",
            "\u001b[90m 3\u001b[39m age            -\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m\u001b[31m52\u001b[39m      54.6     -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m0\u001b[39m\u001b[31m46\u001b[4m2\u001b[24m\u001b[39m  0.963 \n",
            "\u001b[90m 4\u001b[39m black        -\u001b[31m613\u001b[39m\u001b[31m.\u001b[39m      \u001b[4m1\u001b[24m335.      -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m459\u001b[39m   0.647 \n",
            "\u001b[90m 5\u001b[39m hispanic     -\u001b[31m987\u001b[39m\u001b[31m.\u001b[39m      \u001b[4m1\u001b[24m956.      -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m504\u001b[39m   0.615 \n",
            "\u001b[90m 6\u001b[39m married       591.      \u001b[4m1\u001b[24m257.       0.470   0.639 \n",
            "\u001b[90m 7\u001b[39m nodegree     \u001b[4m1\u001b[24m613.      \u001b[4m1\u001b[24m413.       1.14    0.255 \n",
            "\u001b[90m 8\u001b[39m education     531.       284.       1.87    0.062\u001b[4m8\u001b[24m\n",
            "\u001b[90m 9\u001b[39m re74            0.080\u001b[4m9\u001b[24m     0.120    0.675   0.500 \n",
            "\u001b[90m10\u001b[39m re75            0.143      0.186    0.772   0.441 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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ABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIE\nOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJCEj6cLqKimhtRyU88aIbQ6\nnTCbi0wmN+1CCPG++7oGAAAewogdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAA\nAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDY\nAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABI\ngmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcA\nACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmC\nHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACA\nJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCR8PF2AZxiNRid70Gg0LqmkpJ7dvgvhiiNgg0aj\ncWv/bqXT6YQQer1eWahwfHx8dDpdxT3+QgitVltx69dqtQaDwWQyebqQsvDx8RFC+Pr6uvWH\nj5Mq6P+YQLmppMHO+R9bbv+5pxFu3YNbf3BrNBpv/sVgm1J5hX4Lws2fbzmgfo+Q48sPVHKV\nNNjl5OQ42YPJZHZJJcVphNDohDC7cRfCFUfABqPR6Nb+3Uqr1fr6+ubl5RUUFHi6lrLQ6/W+\nvr4V9/gHBASYTKaKW79er8/LyysqKvJ0IWWk1+vz8/Pz8vI8XUipDAaDp0sAvBrX2AEAAEiC\nYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAA\nIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACS8PF0AfCMZ5NT3Ne54cat\nvLw89/U/v24t93UOAEDFxYgdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg\n2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AEonTT8AACAA\nSURBVAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYA\nAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg\n2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAA\nSIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAH\nAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJ\ngh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASMJ7g93BgweHDBky\nZMiQDRs2FF9rMpn27dv36quvjhs3bvjw4dHR0W+//favv/5a/nUCAOBaX375Zffu3atUqeLr\n61ujRo19+/Z5uiIP+PLLLzUajUajeeuttzxdi8t88sknypuaN2+em3bh46Z+nZSamrpkyZLS\n1hYUFMydO/fYsWNCCIPBULVq1bS0tPj4+Pj4+GHDhk2YMKEcKwUAwJU++eSTyZMnq/+8fv16\nWlqaB+vxiMOHDz/22GNCiJEjR7766queLsdlnnjiiYSEhMWLF7/wwgtNmzYdOnSoy3fhpSN2\nixYtysjIMBgMJa794osvjh07ptfrZ8yYsW7dus8+++zLL7989NFHNRrNli1b4uLiyrlaAABc\nZcGCBcpC7969V61a9dVXX7Vv3778y3jyySc1Gs27775b/rtOS0sbM2ZMXl5ew4YNly9f7iVV\nlUGJ1c6fP79169Ymk+mxxx67ePGiy3fqjcHuu+++O3r0aIsWLVq0aFF8bUZGxtatW4UQEyZM\niIyM1Ol0Qgi9Xh8VFTVgwAAhxJo1a8xmcznXDACA88xm87lz54QQer1+y5Yt48ePHzlyZFhY\nWPlXEh8fX/47VUybNi0pKUkIsWLFiuDgYMtVHqyqDEqs1mAwrF69WqvVpqamKqOSruV1wS4l\nJWXFihU+Pj5PPfVUiQ0OHDhQWFjo7+/fp08fq1VDhgwRQly5cuX06dNuLxQAAFfLzs7Oz88X\nQtSsWTMkJMSDZZw8edIjuz5y5Mjq1auFEIMHD77//vu9pKoysFFthw4dHn30USHE7t27N23a\n5Nr9elewM5vNCxYsyM3Nffjhhxs1alRimz/++EMI0bp1ax8f6wsE69SpU716dbUNAAAVi3rG\nSTkf5Sk///xzYWGhR3b9/PPPKwfhnXfesVrlwarKwHa1b731ll6vF0LMmjXLZDK5cL/eFew2\nb978+++/N2/ePCoqqrQ2yvBsvXr1Slxbt25dIURiYqJ7CgQAyCw7O3vp0qWDBg1q2LBhQECA\nclNqz54958yZc+3atdK2Kioq+uKLLx588MEmTZoEBgb6+PiEhITceeedU6ZM+eWXX+zc9axZ\nszQaTVBQkPLPpKQkzf9s2bLFyQoVP/zww6RJk5o1axYUFBQQENCsWbMnnnjit99+s2zz+uuv\nazSaXr16Kf988cUXlRr69etn1du+ffsmTZrUsmXLkJAQvV5fu3btbt26vfLKK//880+Je+/Z\ns6dGo9FqtWazOTMzc9q0aTVr1jQYDHPmzFHbHD16VLlQvk+fPhEREfZXZU/nioSEhKlTp7Zr\n1y4kJMRgMNSrV69Xr17vv//+jRs3bBw6hw67Pcewbt26Dz30kBDi7Nmz27dvt7FrR3nRXbFJ\nSUlr1641GAwzZszQaktNnBkZGUKI0gaoq1atKoRIT093U5EAAFkdO3YsKirKKpdcv379wIED\nBw4cWLBgwddffx0ZGWm1VXJy8qBBg6zm20pLSzt+/Pjx48cXL148Y8aMf//73x6sUAiRnp4e\nHR2tXKGuOnv27NmzZ1esWPHCCy8UHx6zISMjY+zYsdu2bbN8MSUlJSUl5ciRI/PmzXv33Xen\nT59utZXRaBRCmM3mnJycIUOGqHO43Lp1S23z8ccfKwuTJk2yvx47O8/Pz582bdrSpUstN0xO\nTk5OTo6Li3vvvfeWLVs2YsSI4p2X+bDbNnny5M8//1wIsWzZMuVaMpdwV7AzmUwmk0mr1dqI\naJaKioo+/PDDgoKCyZMnK6NupcnJyRFClHbDrDKwmZ2dbfV6VFRUUVGRsjxgwICJEyfaU5UN\nOp2bBzs1GrfuwqeUA+gSGo2mtA/IJZT47ibKNzYoKKiC3oKj/F2o/I9QQel0Ord+xG6l1WqD\ng4Mr7pdHCBEQEODv7+/pWkrl2pNWlq5du9a/f//r168LITp27Dh+/PgmTZr4+fklJiYuWrTo\nl19+uXHjxtChQ0+fPm11ymjUqFFKqlO2atasmV6vv3r16g8//LBmzZrMzMwPP/ywcePGTz/9\ntO0CnnvuuYkTJ2ZnZ7dr104IUa9evf379yur6tSp40yFRUVFQ4cOVXpr1KjRo48+2qxZs4yM\njPj4+NWrVxcWFs6dO9dgMMyePVsIMXXq1HHjxi1btkyZaG3mzJnK3CsBAQFqbwMGDDhw4IAQ\nom7dulOnTu3WrVtQUNDly5e3bdu2YsWKvLy8GTNm6PV6q2vlfX19lYWvvvpq3759BoOhc+fO\nRqNR/aVfWFioRE+j0di/f3/LbW9b1W07F0JER0evX79eCFG7du0pU6Z07NixZs2aFy9e3Lp1\na2xs7M2bN0ePHr158+bBgwc7+cW4bbWK7t27165d+8qVK7t3775165arLql0INgpQ4grV65U\nvmG2vfPOO6+++mr//v137NhhT+dr1649f/58u3btlDtby0z5ear8eLKUmZmpnurOzc21M27a\nZL0LN3DjLoofogrUvys+vlIplSvxyH17cTe3HiJ3U86neLqKMlK/P54upOy8/MvvvmC3ZMkS\n5Zd3r169du3aZfnX6aOPPvrQQw9t2LAhIyNjwYIFH3zwgboqISFBiTjt27c/ePCg5VajRo16\n+umnu3TpkpGR8c4770yZMsX2ga1WrVq1atUyMzOVf/r4+ISHhztfoRDik08+UVJd165dd+/e\nrcaLJ5544pFHHnnggQcKCwvnzJnz6KOPhoWFhYaGhoaGVqtWTa3KqoyPPvpIecstWrT48ccf\na9Soobzevn37AQMG9OvXb9iwYUKI559/Pioqqnbt2uqG6pXxS5cu7dSp0zfffGMVJ+Lj42/e\nvCmE6Nmzp1UGum1Vt+38888/V1Jdu3bt9uzZo3bVoUOHIUOGREVFDR06tKio6Mknn4yMjAwM\nDHTmsN+2WoVWq+3Tp8/q1avz8/P37Nnz4IMPFm9TBg4Eu++++04IkZWVZU/jBg0aCCESEhLs\naXzmzJlNmzb5+/tPnTr1tj9Q/P39MzMz8/LySlyrvF78z81du3ZZ/lP5kJyhjv+5nEYIrU4n\nzOYit/38EkLk5+a6r3ODwVDaB+QStq+EcFJAQICfn196enpBQYH79uI+er3e19fXzv9PvVD1\n6tULCwstz55ULMHBwZmZme77+eBWfn5+AQEBNn7AegODwaCOzbiWn59fv379rly5MnPmTKtz\nDhqN5plnnlEeg7Rnzx7LVeokDP379y9+pqJly5YLFy5MTExs1KhRXl6ecrqwnCsUQqgPOfjk\nk0+sAtM999wzbty4VatWFRYWrl69+rZTAZvN5v/85z/K8qJFi9RUpxo6dOjw4cM3b96clZW1\nevXq559/Xl2l/sH266+//vnnn8UHiQ4fPqwsdO3a1XYZxd22c+Vcs0aj+eKLL9TIpRo4cOD4\n8eM/++yz5OTkDRs2KLesKsp82O3RpUsX5RbgI0eOeCDYOeTPP/8U9v0CzsvL+/DDD00m0xNP\nPFH8K1JclSpVrl69mpqaWuJaJex78BZxAEBF9Pzzz1umECstW7ZUFpKTky1fV8cRTpw4UeKG\nLpyorGwVJiQknD9/XggRERHRpk2b4hvOnDmzd+/e1atXb9q06W1rOH78+IULF4QQDRs2vPfe\ne0tsM2bMmM2bNwshvv322xILHjx4cIkz86mDQXfeeedtKylNiZ2fOXNGieDdu3dv1apViRs+\n8sgjn332mRBi27ZtlsGubIfdTuo7PX78eBk2L9Ftgl3xyZ2XLVtWPOpaKiwsPHv27Lp164QQ\nVvMKlujgwYPJyck6nW7r1q1W13VevnxZCLFt2zblHpkPPvhAr9c3atTor7/+KvGmG7PZrEzi\n3KRJk9vuFwAAGwoKCrKzs5UrfNRRzNz/e7qjR48efn5+OTk527ZtGzdu3PPPP9+2bVuvqlB5\n/KYQomPHjiV20rp169atW9u5R7W3Ll26lHaGrVOnTsrCb7/9Zjabizfr2bNniRuqM1qUNt+Z\nPUrs/ODBg8pCidFWoR6f255stOew26lx48bKgjLjh0vcJti9+OKLVq849NjaHj163LaNculb\nUVGR8kdAcampqcr4nHJpRURExO7du0+dOpWfn291hfi5c+eUB+rZ+OQAACjNvn37Pv/88/j4\n+CtXrty8efO298GEhoYuXrx44sSJJpNp7dq1a9eubd68eWRkZGRk5L333qtMrerZCtW0ZPvG\nRDv9/fffyoKaSIpTB8zS09MzMjKqVKli1cDywjtLymiOcK7UEjtXh9OWLl1qdVdscep7tOTo\nYbdT7dq1dTpdUVFR2Qb8SnSbYDd58uT4+PiTJ0+WYUrAli1bqg+8s6FPnz7FnyGhePXVV48f\nPx4dHW15+3H37t2XLl2am5u7Y8cO5QpN1caNG4UQ4eHhHnn6CgCg4srMzIyOjlbOITrkscce\nCwsLe/XVVw8dOiSEOHPmzJkzZ5YuXarVanv16vXkk0+OHDnSJfcDla1CZY4wUeyWzLJRRk+E\nEJa3F1jRarXKKKYQIj09vXiwK+2yK/XGEWdKLbHz0i7fKlF+fr7lyFGZvxj20Gg0fn5+mZmZ\nxWfzKLPbBDsl2GZnZ//888/KVHszZ860fSpWCBESEhIeHq4+yNW1jEbjQw89tHr16jVr1gQG\nBip7yc7OXr9+vTLWOmHCBJfvFAAgt8cff1z55R0UFDRz5sxBgwbVq1cvNDRUuVcjNzfXz8+v\ntG3vvffee++996effvrmm2927tz566+/KnN+7d+/f//+/f/5z382b95cs2ZNj1SoZsqynSss\nG3VAq8TTtaXNx6Se1nRmwqwSO1cPwvjx4y2vnyuNZXpx5othD6PRmJmZaTKZCgoKXHJjkF03\nT/j7+6snrSdPnlziXbvlKSoq6u+//1b+b1m2bFlQUFBqampRUZFGo5k4caLlXNUAANzWyZMn\nv/rqKyGEv7//wYMHi1/PY8+dznfdddddd901Z86cmzdv7t27d9OmTRs2bCgoKDh06NCoUaPU\nKXPLuUL1YvfbPpfCHuq9iTYeBFBUVKSGSHsutVepeS4vL8+1k3GqZVSrVu2ee+6xf0OXfDFs\nU46VVqt11e3eDtwVq0xdGBoa6pIdO0Or1T7zzDNdunTZtWvXX3/9lZqaGhIS0qpVq2HDhtlz\nUw8AAJaU+byEEKNHjy7xKu3SrgIvUWho6IgRI0aMGPHyyy/fd999KSkp+/fv//HHH9VnTJVn\nhXfccYeykJKSUua9qxo2bKgsnDt3rrQ2aiVVq1a1cca2OPUMbFZWlvpoNZdQD8LZs2cd2tC1\nX4zilEdliJKmaSszB4Ld66+/7qq92umtt96ysbZHjx723JwBAIBt6mX76uwVViyf1mq/1q1b\nx8TEvPbaa0KIhIQEZ4JdmSvs0KGDsnD48OES71E9ffr0/PnzhRBt2rSZNm2a7TI6d+6sLMTH\nxyvPlyreJj4+3qqxnerUqaPkxeTk5NJusCibu+66S1mIi4srfuelDW76YqiuXLmijPnZ8+gH\nO1XU6d0BAHAV9SSgMhmqleTk5A8//FBZtryV0GQyvfTSS3379n344YdL61kdhXLySqyyVSiE\naNWqVfPmzYUQKSkp33zzTfFtP//88xUrVqxYsaLEc7VWvbVp00a5HCs5OVkdzbKyatUqZSEq\nKsrWWypGneVEvZO3NI7e0BkeHq7MGHfr1i21PCv79+9v2rTp9OnTLackLPNht7NadcDPmRle\nrJRlguJbt24dP3786tWr6jwuNthzlSIAAB6knmXbunXrm2++qT6fSghx8eLFgQMHNmzYUKvV\nXr9+PSsrKzU1VXmcsVarPXDggDLTar9+/aKjo626zc7OVp4rIITo1q2b5apnn31WuVdg5syZ\n9vxSL1uFimeeeUZ5VumUKVPuvPNOy4kjjh07pkQTHx8fy7mU1WvprM5dKs9aUB4CO3Xq1EOH\nDlndhbpixYrdu3cLIWrVqjV27Njbvi9L6hSAx48fLzEUllaVPWbOnDlu3DghxHPPPdexY0er\nWf0uXLjw+OOPnz9/fuHChZafozOH3Z5q1XmJXTj9oWPBLikpafr06du2bbP/akGCHQDAyw0a\nNCg0NPTmzZunTp3q27fvzJkzGzZsmJKSsnPnzqVLl+bn5//0008xMTHKM1JffPHFmJiYqlWr\n1q9f/+23346MjCwqKho/fvzatWuHDh3aoEGDwMDAW7du/frrr2vWrFEGn0aOHGn1wINly5Yp\nj/4bN26cPcGuzBUKISZOnLhu3bp9+/ZdvHjxzjvvfOyxx9q1a5eTkxMfH7927Vrl8Ykvv/yy\n5dz+6l2S69ata9CgQbNmzS5evDhr1iytVjt58uSNGzfu2bPnr7/+6tChg3LJu9FoTEpK2rBh\nw5dffimE0Ol0q1atcugCO2GRfY8cOVJiAxtV3bbzsWPHbtmyZcOGDenp6T169Jg0aVLfvn2r\nVq165cqVuLi4zz77TJkX5oknnlBPXjt52O2pVj1tXYanqJVGY/8ke1evXu3QocOlS5cc2oGr\nJvFzLeefFfv8T0ddUklx5fOs2KL6Dd3XubufFTu/bi33da48KzYtLY1nxXoEz4r1IOVZsRkZ\nGV7+rFjXXlav+uabb0aOHJmfn2/1enBw8NatW3v37r148eIpU6aor7/wwgvKw5nWrVs3adIk\ndQ624h588MHVq1dbXR0fGBio/H96+PBhy1/qmZmZyhsMCwuzOiNZ5gqVbseOHVviqViNRjNr\n1izlUaqqoqKiNm3aqE/CVRQUFChDVllZWePHj1fmji0uNDR09erVAwcOtHp92LBhyvOl4uLi\n7r777uIbFhQU1KpVKzU11c/P7/r168XvJ7BR1W07V1rGxMR8+umnJSYTrVb79NNPz58/32qm\ntjIfdtvHUAhhNpvr1at3+fJlX1/fq1evuuppqA6M2M2bN09NdW3atImIiAgODnbHTHUAAJSz\nIUOGHDly5IMPPvjhhx+uXr2q1+ubNm06YsSIyZMnK2cbJ0+efOnSpc8///zq1asNGzZUn/I5\nevToyMjIzz77bPfu3WfOnLl+/XphYWFQUFBYWFjXrl3HjRtXWs4otwqFEIGBgVu3bt25c+fn\nn39++PDhlJQUk8lUr169yMjImJiYdu3aWe1Lp9Pt3Llz+vTpBw4cSE9Pr169eps2bdShpoCA\ngA0bNvz444+xsbEHDhxITk7Oz88PDQ2NiIjo37//xIkTi09KbA9fX9+hQ4euWrUqJyfnv//9\n74MPPuhQVfb0/8knnzz11FOfffbZ/v37//nnn8zMzMDAwDvuuKNXr16PP/54idOllfmw37ba\nQ4cOKTdn3H///S58xr0DI3YRERG///57UFDQtm3bevfu7aoKPIIRO0bsSsOInWcxYudBlXzE\nDt4gPj5eGb/s27fvzp07PV2Oe40fP165BHPLli1Dhw51VbcO3BWrjAlPmTKloqc6AADghbp0\n6aIMcO7atevUqVOeLseNLl++vG7dOiFEeHj44MGDXdizA8FO+RvO6kYSAAAAV3n//feFEGaz\n+eWXX/Z0LW40e/Zs5bq9d9991yWPElY50JfynDvLe30BAABcqFu3bo888ogQYsuWLXv37vV0\nOW7x22+/ffbZZ0KIe++9t/ilhE5yINjde++9QogzZ864tgIAAADVf/7zH+XZZRMmTLDxUNoK\nKi8vLzo6uqioKCQkpLTZkp3hQLCbPn26Vqv99NNPvfm6WgAAUKGFhISsW7fOYDAkJSVNmjTJ\n0+W42MyZM0+cOKHRaFauXNmgQQOX9+9AsOvYsePChQvPnj07atQo+RI0AADwEt26dVNOVn71\n1Ve2HxxfsSxfvnzRokVCiPfee2/YsGHu2IUD050UFRXl5ORs3Lhx2rRper1+3LhxXbt2rVmz\npu2r7lw1f49rMd0J052UhulOPIvpTjyI6U4ACThwJ4RVgFMffGubdz55AgAAQD6uvMMWAAAA\nHuTAiF3v3r2NRqOPj49Op9NoNO6rCQAAAGXgQLDbv3+/28oAAACAszgVCwAAIAmCHQAAgCQI\ndgAAAJJw4Bq7I0eOONR1Xl5eVlbWgAEDHCwJAAAAZeFAsOvWrVsZdsA8dgAAAOWDU7EAAACS\ncGDEbuDAgTbWFhYWXr169eTJkwUFBVWqVImOjg4ICODBLwAAAOXGgWC3ffv227bJyMhYvnz5\na6+9dvTo0c2bN9epU8eJ2gAAAOAAF5+KDQoKeuaZZ7777ruff/65X79+FfdJ5AAAABWOAyN2\n9uvRo8fYsWNjY2NXrFgxdepUd+wCAACXyMjIcEe3XIwEj3BLsBNC9OvXLzY2NjY2lmAHAPBy\nPju2uLbDwgHDXNshYCd33RUbHBwshDhz5oyb+gcAAIAVdwW7S5cuCSHy8/Pd1D8AAACsuCXY\nFRUVrVq1SghRrVo1d/QPAACA4hy4xu7ixYu2GxQVFaWnp588efLjjz8+ePCgEKJTp05OVQcA\nQCVTVFT0xRdfrF69+rfffktLSwsNDe3atetTTz3Vp08fT5eGCsCBYNegQQNHe3/qqacc3QQA\ngEorLy/vwQcf/Pbbb4UQ/v7+tWvXvnr16tatW7du3frss8/OmzfP0wXC27nrGjutVjtnzpz+\n/fu7qX8AAOQze/bsb7/91s/Pb/Xq1bdu3fr7779TU1Pff/99jUYzf/78devWebpAeDsHRuxa\nt25tu4FGozEajTVr1mzfvv3DDz/cqlUr52oDAKASuXHjxocffiiEmDdv3iOPPKK86Ofn99xz\nzyUlJS1evPjll18eNWqURqPxaJnwag4Eu5MnT7qvDgAAKrmvv/46Pz8/ODh44sSJVqumT5++\nePHi8+fPHzx48O677/ZIeagQ3DVBMbyc7uLf7uvcrNPqikzu61/UreXGzgHAQw4dOiSE6Nmz\np16vt1oVHh5ev379ixcvHjp0iGAHG9x1jR0AAHCIcmasefPmJa5t1qyZECIhIaFca0JF49SI\nndlszsjISE9PF0KEhIQEBga6qCoAACqdGzduCCFq1Sr5pETt2rXVNkBpyhLsrly5Ehsbu2PH\njt9++01JdYrQ0NBOnTpFRUWNGzcuICDAdUUCACC/jIwMIYSfn1+Ja5XXLX/tAsU5fCp2yZIl\n4eHhs2bN+vHHH62+Xjdv3ty1a9eTTz4ZHh6+c+dO1xUJAEBlZzabhRDcEgvbHAt2CxYsiImJ\nycrKsnzRz8/P6s+LK1euDBo0aMeOHS4oEACAyqFKlSpCiOzs7BLXKq8rbYDSOBDs/v7771mz\nZinLw4cP/+qrr86fP19UVJSdnZ2dnV1YWHj27NnPP//8/vvvF0IUFRVFR0cro8oAAOC2atSo\nIYS4cuVKiWuTk5NF6VfgAQoHgt2yZcvy8vJ8fX23bt26adOmkSNHNm7cWKv9/3rQ6XTh4eFj\nx479/vvvP/30U41Gc+PGjeXLl7unbAAAZNO2bVshxOnTp4uvMpvNyusdOnQo77JQoTgQ7Pbu\n3SuEmDhx4pAhQ2y3fPzxx0ePHi2E4Eo7AADs1KtXLyFEXFxcTk6O1apffvnl2rVrQoh77rmn\n/AtDBeJAsDt//rwQYvDgwfY0HjFihBDi999/L1tZAABUNg8++GBgYGBWVtbHH39steq9994T\nQnTq1KlNmzaeKA0VhgPBLjU1VQhRp04dexqHhYUJptsBAMBugYGBL7/8shDipZdeWrlyZUFB\ngRAiPT39ueee+/rrr4UQ8+bN83CJ8HoOBDvl1lc774fIzc0VQhR/KAoAACjNc889N27cuLy8\nvAkTJoSEhDRs2LB69erz5s3TaDQLFy7s3bu3pwuEt3Mg2CljdYcPH7ansdKsbt26ZSsLAIBK\nSKfTrVmz5quvvurTp4+fn9+VK1dq1qw5evTo+Pj4qVOnero6VAAOPHni7rvvPnPmzMKFCx97\n7DHlluzSXL16dcGCBcomzhYIAEAlM3LkyJEjR3q6ClRIDozYjR07VgiRnJzcq1evPXv2lNjG\nZDLt2LGjR48ely5dEkJER0e7pEoAAADclgMjdpGRkYMHD962bdsff/xx//33h4WF3XXXXY0b\nNw4MDDSbzRkZGefOnTty5Mjly5eV9iNGjFDu3AYAAEA5cCDYCSHWrl07YMCAAwcOCCGSkpKS\nkpJKa/nAAw/ExsY6Wx0AAADs5liwCwoK2r9//0cffbRw4cLExMQS2zRr1mzGjBmTJ0/mQcUA\ngAqhcMAwT5cAuIZjwU4IodPppk+fPm3atOPHjx87duzvv/9OS0vTaDTBwcENGza86667IiIi\niHQAAADlz+Fgp9BoNHfeeeedd97p2moAACh/0/6+5NoOFzas59oOATs5cFcsAAAAvFlZgl1S\nUtJbb731559/Fl+1cOHCV155RXmqLAAAAMqTY8HObDa//vrr4eHhr7322tmzZ4s3OHHixNtv\nv92iRYs33njDRRUCAADALo5dYzdr1qz3339fWb5+/XppzQoKCl5//fW8vLx33nnHqeoAAABg\nNwdG7H799dcPPvhACOHj4/Poo4926tSpeJtnn332pZde8vPzE0K8++67AivwhQAAIABJREFU\nCQkJrioUAIDK4+LFi3379tVoNBqN5tatW54uBxWGA8FuyZIlZrPZx8fn+++/X7lyZevWrYu3\nadmy5dtvv71v3z4fHx+z2bxo0SLXlQoAQKWwcuXKiIiIXbt2eboQVDwOBLv9+/cLIaKjo++5\n5x7bLbt06fLwww+rmwAAAHtcvnx54MCBEyZM0Gg0EyZM8HQ5qHgcCHaXLl0SQnTt2tWexkoz\nZRMAAGCP9evX79ixIzIyMiEhYfjw4Z4uBxWPA8FOq9UKIYKCguxp7O/vr24CAADsYTQaP/jg\ngz179jRo0MDTtaBCcuCu2Lp16549e7bE6euK++2334QQtWrVKmNdAABUPk888QRjInCGA9+e\nnj17CiFWrlyZlZVlu2VSUtKqVauEEN26dXOiNgAAKhdSHZzkwBdo3LhxQojExMQHHnjg5MmT\nJbYxm81bt269++67lXuzlU0AAABQDhw4FRsZGTl27Ni1a9cePny4TZs2bdu2bd++fd26dQMC\nAnJzc69du5aSknL48OGUlBSl/ZAhQ/r27euesgEAAGDNsSdPLFmy5OLFiz/88IMQIiEhwcb8\nw5GRkWvXrnW2OgAAANjNsXP5VapU2bNnz6JFi+64447S2jRv3nzZsmW7d+8ODAx0ujwAAADY\ny7EROyGETqeLiYmJiYlJSEg4duxYYmJiRkaGVqsNDg6+4447OnTo0KpVK3cUCgAAANscDnaq\ntm3btm3b1oWlAAAAwBncVg0AACAJgh0AAIAkCHYAAACSKPs1dgAAwLVq166dm5urLBcWFioL\nYWFhGo1GWZ4xY8bs2bM9UxwqAoIdAADe4tatW3l5eVYvpqenq8s5OTnlWxEqGIIdAADeQh2u\nA8qGa+wAAAAkUUlH7IxGo5M9qJc7uJzasft2UQ7cWrzzH58NOp1OCKHX65WFCsfHx0en07n1\nELmbVqutuPVrtVqDwWAymTxdSFn4+PgIIXx9fb35h08F/R8TKDeVNNg5/6PBnT/3NMp/vPcn\n6+1pNBqz+3p36092rVar/rci0mq1Go2mov/yq7j1azQa5SPwdCFloX75vfn4V9BjC5SbShrs\nsrKynOzBZHJXcNEIs0anE2Y37sLddDqzW4t3/uOzISAgwMfHJzc3t6CgwH17cR+9Xu/r6+vW\nQ+RWfn5+JpOp4tbv4+OTk5NTVFTk6ULKws/Pz9fXNy8vr/jF+97DYDC4Y0B3YcN6Lu8T8IhK\nGuwAAFAEBQV5ugTAZSrq+SYAAABYIdgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJg\nBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAg\nCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0A\nAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQI\ndgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAA\nkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgB\nAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiC\nYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAA\nIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYId\nAACAJAh2AAAAkiDYAQAASIJgBwAAIAkfTxfw/yssLNy9e3dcXFxiYmJ2dra/v39YWFiPHj36\n9Onj6+tr1dhkMv3www979+69cOFCVlZWUFBQ8+bNBwwY0L59e48UDwAA4HHeEuxSU1Nnz56d\nmJgohNBoNFWqVElPTz958uTJkyd37tw5Z86c4OBgtXFBQcHcuXOPHTsmhDAYDFWrVk1LS4uP\nj4+Pjx82bNiECRM89S4AAAA8yCuCndlsfueddxITE41G4+OPPx4ZGanX63Nzc3fs2BEbG5uU\nlLR8+fKZM2eq7b/44otjx47p9fqYmJhevXrpdLr8/Pzt27fHxsZu2bKladOmPXv29ODbAQAA\n8AivuMYuISHhzJkzQoinn366b9++er1eCGE0GqOiogYNGiSEOHToUG5urtI4IyNj69atQogJ\nEyZERkbqdDohhF6vj4qKGjBggBBizZo1/6+9ew+Oqr77OP7dS3aThYQkRC6hGgpEKEmKiBRl\nCS2owVKLMWW80gSDlwoarQ9Mx6mtOCKUS0XQTkEUoajjKFAoN8UIkYQOSlS8EmwCCWBAjCxJ\nlpDs9fnj9NnJkxsE3Jyzv7xffzCHc357zne/CctnzzUYDOr1XgAAAPRiiGDndrvT0tIGDx48\nduzYFotGjRolIj6f79SpU9qckpISn8/ncDiysrJaDJ4yZYqInDx58uDBg+GvGgAAwFgMcSjW\n6XQ6nc42F5lMJm1C240nImVlZSKSlpZmtbYsvn///klJSTU1NWVlZcOHDw9bvQAAAEZkiD12\nHdCukOjfv3+/fv20OVVVVSIyYMCANscnJyeLiHYRBgAAQLdi6GBXUVGxY8cOEcnLywvNrK+v\nF5H4+Pg2X5KQkCAidXV1XVIgAACAgRjiUGybKisr586d6/P5brzxxubn3p07d05E7HZ7m6/S\njtg2NDS0mD99+nS/369NX3/99dOmTbvE8izmMGdikynsmwij8BbfXqz/QZjNZhHp2bNnhF6C\nYzKZTCZT61s/RhCLxRLWH3FYWSyWuLi4CP3l0X75HQ5HTEyM3rW0KxAI6F0CYGgGDXb79+9f\nvHhxY2NjZmbmrFmzLvyF2udp6My8kLKyMp/Pp02np6e3Pj+v01pt4ofXBZsIn3AW/wP8+M5H\nu9o6cpkj+FuBmEymLvgRh0+k//IYvP7QJzmANhnx03PDhg3/+Mc/gsHgrbfeOn369BYpzeFw\nuN3upqamNl+rzXc4HC3m79u3r/lfa2pqLrHI0P6/H5xJxGyxSDDoj9gvphaL2e8PY/GX/uPr\nQI8ePWJiYmpra71eb/i2Ej42my0qKurs2bN6F3KRkpKSfD7fmTNn9C7kIvXq1cvtdofv8yGs\nYmJievToUV9f394HrBHY7fbY2Fi9qwCMy1jBzuPxLFu2rLi42GazzZw5c+LEia3HxMXFnTp1\nyuVytbmG06dPS5gP1QEAABiTgYKdx+OZN2/egQMHEhISnnjiidTU1DaHDRw4sLy8/NixY60X\nBYPB48ePi8jgwYPDWysAAIDxGOVEHJ/PN3/+/AMHDgwYMODZZ59tL9WJSHp6uoh89dVXHo+n\nxaKKiora2loRycjICGu1AAAABmSUYLdmzZqPP/64T58+zzzzTO/evTsYOXbs2OjoaO1Jsi0W\nbdiwQUSGDBmSkpISxloBAAAMyRDB7vDhw1u2bBGRmTNnJiYmdjw4Ojr6tttuE5F169YVFhZq\nJyk3NDS88sore/fuFZH8/PzwlwwAAGA4hjjHbuvWrdptShYtWtTemKlTp06dOlWbzsnJOXr0\naFFR0fLly1euXBkbG+tyufx+v8lkuvfee7VjtQAAAN2NIYJd6NL61jcWDml+7wmz2fzYY4+N\nGTNm586d5eXlLpcrPj5++PDh2dnZHZycBwAAoDZDBLs5c+bMmTOns69yOp1OpzMc9QAAAEQi\nQ5xjBwAAgEtHsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDs\nAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAU\nQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMA\nAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATB\nDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABA\nEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsA\nAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ\n7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAA\nFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbAD\nAABQBMEOAABAEQQ7AAAARVj1LkAfdrv9EtdgMpl+kEr03UT4hLX4S//xdcBisYhIVFSU2RyR\nX3usVqvFYglri8LNZDJFbv1ms9lmswUCAb0LuRhWq1VEoqKi9C6kI9q/UADt6abBTvv8uhTh\nzC0m7Y8IjnViMpmC4Vt71I7N4Vu5mM0+s9ni95uD4XoLwV//JkxrFhGLxWI2my/9N1xHJpMp\ncus3mUzaj0DvQi6GVrbBf38i+hsv0AWM+683rM6ePXuJawgEwvW/vkmCJotFgmHcRLhZLMGw\nFu/z+cK3cqvVajab/X5/+Ha6NF7yr18HbDZbVFTUpf+G6yUmJiYQCERu/Var9dy5c36/X+9C\nLkZMTExUVFRTU1NTU5PetbTLbrdHR0frXQVgXBH5tRIAAACtEewAAAAUQbADAABQBMEOAABA\nEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsA\nAABFEOwAAAAUQbADAABQBMEOAABAEVa9CwA6rSC+b/hWbjabTCZzIMYfDIZrE4vCtWIAQHfH\nHjsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAA\nAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDs\nAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAU\nQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMA\nAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATB\nDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABA\nEQQ7AAAARRDsAAAAFGHVuwCg2/mf6m/Dt3Kz2Ww2m30+X/g28dfkvuFbOQDgUrDHDgAAQBEE\nOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQRKTeoDgQCLz//vu7\ndu06cuTI2bNnY2Njhw4dOnny5JEjR+pdGqC4sN5gOfp0bSAQ8Hg84dtEpN9gOXz9t1qtVqvV\n6/X6/f4wbSLSmw8YX0QGO6/Xu2DBgtLSUhGx2+0JCQm1tbUffPDBBx98kJ2dnZ+fr3eBAAAA\nOojIYPf666+XlpbabLZZs2aNHz/eYrF4PJ6tW7euXbt206ZNqampmZmZetcIAADQ1SLvHLv6\n+vrNmzeLSH5+/oQJEywWi4jYbLacnJzJkyeLyLp164LBoM5VAgAAdLnIC3YlJSU+n8/hcGRl\nZbVYNGXKFBE5efLkwYMH9SgNAABAT5EX7MrKykQkLS3Nam15HLl///5JSUmhMQAAAN1K5AW7\nqqoqERkwYECbS5OTk0WksrKyK0sCAAAwgsgLdvX19SISHx/f5tKEhAQRqaur69KaAAAADCDy\nroo9d+6ciNjt9jaX2mw2EWloaGgxf/r06aE7M11//fXTpk27xDIs5jBnYpMp7JsIo0gu3iQi\nYjabJWxX4FhstnCtWsRkMolWf8Qym822cLaova+FPwiLxRIXFxfW67dsNa4wrVn75bFardpF\naeFw6c0PBAI/SCWAqiIv2HVM+zzVPp6aKysr8/l82nR6enrr8/M6a9VNky5xDQCUFL5UpFk1\nLDWs6ze40Cc5gDZFXrBzOBxut7upqanNpdp8h8PRYv6+ffua/7WmpiZM5V06s9mcmJjo8Xgi\n94ByQkKCyxWunQrh1qNHj5iYmNraWq/Xq3ctF8Nms0VFRZ09e1bvQi5SUlKSz+c7c+aM3oVc\npF69ernd7vA9uSGsYmJievToUV9f394HrBHY7fbY2Fi9qwCMK/KO18TFxYlIe7nh9OnTEuZD\nLQAAAMYUecFu4MCBInLs2LHWi4LB4PHjx0Vk8ODBXVwVAACA7iIv2KWnp4vIV1991fox4RUV\nFbW1tSKSkZGhQ2UAAAC6irxgN3bs2Ojo6MbGxu3bt7dYtGHDBhEZMmRISkqKHqUBAADoKfKC\nXXR09G233SYi69atKyws1E5SbmhoeOWVV/bu3Ssi+fn5OpcIAACgh8i7KlZEcnJyjh49WlRU\ntHz58pUrV8bGxrpcLr/fbzKZ7r33Xu1YLQAAQHcTkcHObDY/9thjY8aM2blzZ3l5ucvlio+P\nHz58eHZ2dmpqt77DEwAA6M4iMthpnE6n0+nUuwoAAACjiLxz7AAAANAmgh0AAIAiCHYAAACK\nINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEA\nACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJg\nBwAAoAiCHQAAgCJMwWBQ7xrw/5w9e3bZsmVXXnnl1KlT9a6lO9q1a9e+ffumTZt2xRVX6F1L\nd7RgwYK+ffvm5+frXUh3tH///nffffeWW25JS0vTuxYAF4k9dobT2Ni4cePGffv26V1IN/XF\nF19s3LixpqZG70K6qU2bNhUVFeldRTdVUVGxcePGY8eO6V0IgItHsAMAAFAEwQ4AAEARBDsA\nAABFcPEEAACAIthjBwAAoAiCHQAAgCIIdgAAAIqw6l0A/isQCLz//vu7du06cuTI2bNnY2Nj\nhw4dOnny5JEjR+pdmoH4fL7CwsLi4uLKysqGhgaHw5GSkuJ0OrOysqKioloM7lRLDTI4guzd\nu3fhwoUikpub2/pm2gbpp2LNd7vdmzZt2rdv36lTpywWS58+fZxO5y9/+cvY2NgWIw3SUsX6\nD0QELp4wBK/Xu2DBgtLSUhGx2+2xsbG1tbVer1dEsrOzuQu/xuVyPfnkk5WVlSJiMpni4uLq\n6uq0X+CUlJR58+b16tUrNLhTLTXI4Ajicrkeeuih+vp6aSvYGaSfijW/qqrqz3/+s8vlEpH4\n+Hifz+d2u0UkKSlp4cKFl112WWikQVqqWP+BSMEeO0N4/fXXS0tLbTbbrFmzxo8fb7FYPB7P\n1q1b165du2nTptTU1MzMTL1r1FkwGJw/f35lZWV0dPSMGTMmTJhgs9kaGxu3b9++du3aqqqq\nVatWzZ49OzS+Uy01yOAI8sILL9TX19vt9qamptZLDdJPlZrf0NAwd+5cl8uVkZExc+bMAQMG\niMiXX365ZMmSmpqav/3tb3Pnzg0NNkhLVeo/EEE4x05/9fX1mzdvFpH8/PwJEyZYLBYRsdls\nOTk5kydPFpF169axY/Wzzz47dOiQiDz88MOTJk2y2WwiEh0dnZOTc/PNN4vIv//978bGRm1w\np1pqkMER5J133tm/f/+wYcOGDRvWeqlB+qlY8996663vv//+8ssvf/LJJ7VUJyJpaWkFBQVX\nXnllYmKix+PRZhqkpYr1H4ggBDv9lZSU+Hw+h8ORlZXVYtGUKVNE5OTJkwcPHtSjNANxu91p\naWmDBw8eO3Zsi0WjRo0SEZ/Pd+rUKW1Op1pqkMGR4ttvv3355ZetVuvMmTPbHGCQfqrU/EAg\nUFhYKCJ33HGH9pUmZOTIkUuWLCkoKAjNN0hLVeo/EFkIdvorKysTkbS0NKu15ZHx/v37JyUl\nhcZ0Z06nc8GCBUuXLtW++jdnMpm0idD/bZ1qqUEGR4RgMPjcc881NjbeddddAwcObHOMQfqp\nUvO//vrr2tpai8UyevTo8w42SEtV6j8QWTjHTn9VVVUiEjq80kJycnJNTY12xQDapJ2d3b9/\n/379+mlzOtVSgwyOCP/85z+//PLLoUOH5uTktDfGIP1UqflHjhwRkeTk5Ojo6Orq6l27dlVW\nVjY1NfXp02fMmDGjR48OfbcRw7RUpf4DkYVgpz/t0sL4+Pg2lyYkJIhIXV1dl9YUOSoqKnbs\n2CEieXl5oZmdaqlBBhtfVVXVa6+9Zrfbf//735vN7e7sN0g/VWr+yZMnRSQxMfGdd95ZuXKl\nz+cLLXr33XczMjIef/zxnj17anMM0lKV+g9EFg7F6u/cuXMiYrfb21yqHV5saGjo0poiRGVl\n5dy5c30+34033tj83LtOtdQggw3O7/cvXbrU6/VOnz49OTm5g5EG6adKzdfq/Oabb1asWHHj\njTe+8MIL69evX7169e233242mz///PPnnnsuNNggLVWp/0BkYY+d0WkXjjU/1ALN/v37Fy9e\n3NjYmJmZOWvWrAt/YadaapDBunvttdcOHz48YsQI7ZLGi2aQfkZW87V7v9XU1Nx999233367\nNjMpKenuu++Oi4tbtWrVhx9+WF5ePmTIkPOuyiAtjaz+A5GFPXb6czgcItLm/cBC87UxCNmw\nYcO8efMaGxtvvfXW2bNntzgy2KmWGmSwkR06dGjjxo0Oh6OgoOC8/xkbpJ/KNF/+b7+XyWT6\n9a9/3WLR5MmTtXexf/9+bY5BWqpS/4HIQrDTX1xcnIhoN5Rv7fTp09L+qSrdkMfjWbx48dq1\na6Oioh599NF77rmnddToVEsNMtiwmpqali5dGggE7r///uaPN2iPQfqpRvM12nux2+2tk5DF\nYtGOjH/33XfNB+veUpX6D0QWDsXqb+DAgeXl5ceOHWu9KBgMHj9+XEQGDx7c5XUZkcfjmTdv\n3oEDBxISEp544onU1NQ2h3WqpQYZbFh79+6trq62WCybN2/WbjkbcuLECRHZsmVLcXGxiCxe\nvNhmsxmkn2o0X3PFFVeISGNjo/Z85BZLtd3Voa83BmmpSv0HIgt77PSXnp4uIl999VXo3vEh\nFRUVtbW1IpKRkaFDZQbj8/nmz59/4MCBAQMGPPvss+2lOulkSw0y2LC0azD9fv+RVrRHfbhc\nLu2vgUBADNNPNZqvSUtL03Jb6xu/BYPB6upqEenbt682xyAtVan/QGQh2Olv7Nix0dHR2mNP\nWyzasGGDiAwZMiQlJUWP0oxlzZo1H3/8cZ8+fZ555pnevXt3MLJTLTXIYMPKysr6VztGjBgh\nIrm5udpfo6OjxTD9VKP5msTExJ/+9Kci8sYbb7R4DNd7773ndrtF5Oqrr9bmGKSlKvUfiCyW\n5o+Ohi6sVqvJZPr000+/+OKL3r17p6SkmM3mhoaGV199defOnSIye/bsPn366F2mzg4fPrx8\n+XIRmTNnzqBBgzoe3KmWGmRwJNq9e/e33347YsSI4cOHh2YapJ+KNf/yyy8vLCz87rvvKioq\nrrrqqujo6GAwWFRUtGrVKq/Xe/XVV4duGW2QlirWfyCCmHgMsxEEAoHnnnuuqKhIROx2e2xs\nrMvl8vv9JpPp3nvvbX0pXDe0fPly7XGZHVxJN3Xq1KlTp2rTnWqpQQZHnD/96U+ffvppbm5u\nqO0ag/RTsebv3r37+eef9/l8Fould+/ebrdbuw/coEGDnnrqqV69eoVGGqSlivUfiBTssTME\nk8l03XXXXXHFFQ0NDbW1tfX19fHx8ddcc01BQcF1112nd3WGsGfPnqNHj4qIt33Dhw8PnbXT\nqZYaZHDEaXOPnRimn4o1/8c//vHYsWO9Xm9dXZ3L5bJYLIMGDcrJyXnwwQd79OjRfKRBWqpY\n/4FIwR47AAAARXDxBAAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAo\ngmAHAACgCIIdoIitW7eaLtiQIUNaryEYDG7btm3atGmpqamxsbF2u71v374///nPn3766RMn\nTnT9OwIAdJZV7wIAGMKxY8fuvPPOvXv3Np956tSpU6dO7dmzZ/78+QsXLiwoKNCrPADAhSDY\nAYq48sorn3zyyY7HnDlzZtmyZSKSkpLSfH5tbe3EiRPLy8tFJCMj44EHHkhLS7Pb7SdPnnz7\n7bdXr17d2Nj4yCOPmM3mhx56KHxvAQBwiXhWLNCN3HfffS+99FJUVNTHH3+cnp4emv/444//\n5S9/EZFJkyZt2bIlKiqq+avefvvtyZMnB4PBnj17VlVVJSYmdnXdAIALwzl2QHdRVFT08ssv\ni8gf/vCH5qlORN566y1t4qmnnmqR6kTkpptucjqdIuJ2u99+++0uKRYAcDEIdkBXy8zMNJlM\nZrM5GAy63e5HHnmkT58+drt93rx5LUZ+9tlnBQUFI0aMiI+Pt9vtAwYMGD9+/KJFi77//vvO\nbrSxsfGBBx4IBoODBw/+4x//2GJpZWWlNpGWltbmy0PzDx8+3NlNAwC6DOfYAV0tOjpaRILB\n4Llz56ZMmbJ7925t/pkzZ0JjPB7PI488smLFiuYvrK6urq6uLi4uXrhw4cqVK6dOnXrhG336\n6ae//vprEXnhhRe0AppzOBz19fUiEggEOl6Pw+G48I0CALoYe+yRXxzsAAAF6UlEQVSArhY6\n1vnmm2/u3r3bbrePGzfuhhtuSE5ODo3Jzc3VUl2/fv3mzZu3Y8eOjz76aPPmzfn5+RaL5fTp\n03fccceWLVsucIsHDx5cvHixiGRnZ990002tB2RmZmoTpaWl7a1Bmxg3btwFbhQA0PW4eALo\nalOmTNEy2ZgxY/x+/7/+9a/+/fs3H/Dqq6/+9re/FZERI0a89957vXv3br5027Ztt9xyi9/v\nT05OPnToUM+ePc+7xZtvvnnbtm1RUVFffvllampq6wF79+7VEtuoUaN27doVFxfXfOnWrVun\nTJkSDAavv/76wsLCzr9jAEAXYY8d0NXM5v/+u/vkk0/Wr1/fItWJyPz580XEZDK9/vrrLVKd\niPzqV7/Ky8sTkerq6vXr1593c7t37962bZuI/O53v2sz1YmI0+nUdul99NFHGRkZCxcu3Llz\nZ3Fx8VtvvZWXl5ednR0MBtPS0tauXdu5twoA6FrssQO6WnZ29ubNm0XkN7/5TetkdujQoWHD\nhomI0+ksKSlpcw1FRUUTJkwQkZycnA0bNnS8uczMzJKSErvdfuTIkdYhsrnt27cvWbIkdM5f\nyI9+9KPc3Nw5c+bEx8d3vC0AgL7YYwfoJnRmW3OhZz9kZGS098JRo0ZpE5999lnHmygpKdHS\nYW5ubseprq6u7v333y8rK2u9qLq6urCwkBudAIDxEewA3fTr16/1zOrqam1ixYoV7T3pNXQO\n3NGjRzvehHaAVUQee+yxDoZ9880311xzzaJFi06cOJGXl1dSUlJXV9fU1HT06NE1a9YMHTr0\nww8/vPPOOx988MFOv0kAQBci2AG6ueyyy1rPdLlcF74Gj8fj8XjaW3rixAnt7Lprr71WO7zb\nnmnTpv3nP/8Rkeeff37NmjVOpzM2NtZms11++eV5eXmlpaXXXnutiKxYseKNN9648PIAAF2M\n+9gBurHZbK1nhi6tyMvLmz59+nlXYrFY2lu0Zs0av98vIrm5uR2s4YMPPigqKhKRESNGtPko\nWIfDMX/+/IkTJ4rI0qVL77jjjvNWBQDQBcEOMJZevXppE7179/7FL35xKat68803tYlbbrml\ng2F79uzRJrKystobc80112gTpaWlXq+39WPHAABGwKFYwFgGDRqkTWjHRi/aN998c+DAARE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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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QMAAJAExQ4QtWrV6tKliybRV65c8fHx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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### バイアスデータにIPTWを用いたATT, ATEの推定"
      ],
      "metadata": {
        "id": "7CwN91b22yV8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 効果推定"
      ],
      "metadata": {
        "id": "jARxUNc3aPob"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weighting1 <- weightit(data=cps1_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  method=\"ps\",\n",
        "  estimand = \"ATE\")"
      ],
      "metadata": {
        "id": "5aqa2Z5paJDJ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 重みを追加\n",
        "cps1_nsw_data$weights <- weighting1$weights\n",
        "head(cps1_nsw_data, 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "g03XuBvGGIaM",
        "outputId": "1ec4de77-3325-4fff-ba3f-a58af39b8d6b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 5 × 14</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>data_id</th><th scope=col>treat</th><th scope=col>age</th><th scope=col>education</th><th scope=col>black</th><th scope=col>hispanic</th><th scope=col>married</th><th scope=col>nodegree</th><th scope=col>re74</th><th scope=col>re75</th><th scope=col>re78</th><th scope=col>ps</th><th scope=col>ps_match</th><th scope=col>weights</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>Dehejia-Wahba Sample</td><td>1</td><td>37</td><td>11</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td> 9930.0459</td><td>0.24751114</td><td>0.24751114</td><td> 4.040222</td></tr>\n",
              "\t<tr><td>Dehejia-Wahba Sample</td><td>1</td><td>22</td><td> 9</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 3595.8940</td><td>0.07257909</td><td>0.07257909</td><td>13.778073</td></tr>\n",
              "\t<tr><td>Dehejia-Wahba Sample</td><td>1</td><td>30</td><td>12</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>24909.4492</td><td>0.25039795</td><td>0.25039795</td><td> 3.993643</td></tr>\n",
              "\t<tr><td>Dehejia-Wahba Sample</td><td>1</td><td>27</td><td>11</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 7506.1460</td><td>0.47895432</td><td>0.47895432</td><td> 2.087882</td></tr>\n",
              "\t<tr><td>Dehejia-Wahba Sample</td><td>1</td><td>33</td><td> 8</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>  289.7899</td><td>0.44753066</td><td>0.44753066</td><td> 2.234484</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 5 × 14\n\n| data_id &lt;chr&gt; | treat &lt;dbl&gt; | age &lt;dbl&gt; | education &lt;dbl&gt; | black &lt;dbl&gt; | hispanic &lt;dbl&gt; | married &lt;dbl&gt; | nodegree &lt;dbl&gt; | re74 &lt;dbl&gt; | re75 &lt;dbl&gt; | re78 &lt;dbl&gt; | ps &lt;dbl&gt; | ps_match &lt;dbl&gt; | weights &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Dehejia-Wahba Sample | 1 | 37 | 11 | 1 | 0 | 1 | 1 | 0 | 0 |  9930.0459 | 0.24751114 | 0.24751114 |  4.040222 |\n| Dehejia-Wahba Sample | 1 | 22 |  9 | 0 | 1 | 0 | 1 | 0 | 0 |  3595.8940 | 0.07257909 | 0.07257909 | 13.778073 |\n| Dehejia-Wahba Sample | 1 | 30 | 12 | 1 | 0 | 0 | 0 | 0 | 0 | 24909.4492 | 0.25039795 | 0.25039795 |  3.993643 |\n| Dehejia-Wahba Sample | 1 | 27 | 11 | 1 | 0 | 0 | 1 | 0 | 0 |  7506.1460 | 0.47895432 | 0.47895432 |  2.087882 |\n| Dehejia-Wahba Sample | 1 | 33 |  8 | 1 | 0 | 0 | 1 | 0 | 0 |   289.7899 | 0.44753066 | 0.44753066 |  2.234484 |\n\n",
            "text/latex": "A tibble: 5 × 14\n\\begin{tabular}{llllllllllllll}\n data\\_id & treat & age & education & black & hispanic & married & nodegree & re74 & re75 & re78 & ps & ps\\_match & weights\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t Dehejia-Wahba Sample & 1 & 37 & 11 & 1 & 0 & 1 & 1 & 0 & 0 &  9930.0459 & 0.24751114 & 0.24751114 &  4.040222\\\\\n\t Dehejia-Wahba Sample & 1 & 22 &  9 & 0 & 1 & 0 & 1 & 0 & 0 &  3595.8940 & 0.07257909 & 0.07257909 & 13.778073\\\\\n\t Dehejia-Wahba Sample & 1 & 30 & 12 & 1 & 0 & 0 & 0 & 0 & 0 & 24909.4492 & 0.25039795 & 0.25039795 &  3.993643\\\\\n\t Dehejia-Wahba Sample & 1 & 27 & 11 & 1 & 0 & 0 & 1 & 0 & 0 &  7506.1460 & 0.47895432 & 0.47895432 &  2.087882\\\\\n\t Dehejia-Wahba Sample & 1 & 33 &  8 & 1 & 0 & 0 & 1 & 0 & 0 &   289.7899 & 0.44753066 & 0.44753066 &  2.234484\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  data_id              treat age education black hispanic married nodegree re74\n",
              "1 Dehejia-Wahba Sample 1     37  11        1     0        1       1        0   \n",
              "2 Dehejia-Wahba Sample 1     22   9        0     1        0       1        0   \n",
              "3 Dehejia-Wahba Sample 1     30  12        1     0        0       0        0   \n",
              "4 Dehejia-Wahba Sample 1     27  11        1     0        0       1        0   \n",
              "5 Dehejia-Wahba Sample 1     33   8        1     0        0       1        0   \n",
              "  re75 re78       ps         ps_match   weights  \n",
              "1 0     9930.0459 0.24751114 0.24751114  4.040222\n",
              "2 0     3595.8940 0.07257909 0.07257909 13.778073\n",
              "3 0    24909.4492 0.25039795 0.25039795  3.993643\n",
              "4 0     7506.1460 0.47895432 0.47895432  2.087882\n",
              "5 0      289.7899 0.44753066 0.44753066  2.234484"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# weightitで算出した重みがglm()で算出した傾向スコアと差があるか確認。\n",
        "\n",
        "# 対照群の確認\n",
        "print(nrow(filter(cps1_nsw_data, treat==0))\n",
        "- sum(filter(cps1_nsw_data, treat==0)$ps + 1 / filter(cps1_nsw_data, treat==0)$weights))\n",
        "\n",
        "# 介入群の確認\n",
        "print(sum(filter(cps1_nsw_data, treat==1)$ps - 1 / filter(cps1_nsw_data, treat==1)$weights))\n",
        "\n",
        "# OK"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBaJxsYXbGzi",
        "outputId": "4e33fc6d-0198-4e7a-cf27-8f62667be8ab"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] 0\n",
            "[1] -1.126529e-13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# バランシングの評価\n",
        "love.plot(weighting1, threshold=0.1)\n",
        "\n",
        "# 調整前が傾向スコアマッチングの時と値が変わっている。\n",
        "#  -> love.plotの挙動がよくわからない。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "QDY4bdaaIlIc",
        "outputId": "9903a947-e260-4547-eb78-cd6c2596f1dc"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Standardized mean differences and raw mean differences are present in the same plot. \n",
            "Use the `stars` argument to distinguish between them and appropriately label the x-axis.”\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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CUIdgAAACpBsAMAAFAJgh0AAIBKEOwA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\nAAAASUVORK5CYII="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# IPTWで調整後のASAM\n",
        "# 傾向スコアから算出される重みのカラムは作成しておく\n",
        "standardized_mean_error_weighted <- function(df, weights, col, multiple=10){\n",
        "\n",
        "  # 重みのぶんだけ増やすレコード数のカラム作成(今回は重みの10倍)\n",
        "  df$weights_multiplied <- cps1_nsw_data[[weights]]*multiple\n",
        "\n",
        "  # 介入群の調整後のベクトル作成\n",
        "  df_1 <- filter(df, treat==1)\n",
        "  weighted_values1 <- vector()\n",
        "  for (i in 1:nrow(df_1)){\n",
        "    repeated_value1 <- df_1[[col]][i]\n",
        "    repeat_num1 <- round(df_1[[\"weights_multiplied\"]][i])\n",
        "    weighted_values1 <- append(weighted_values1, rep(repeated_value1, repeat_num1))\n",
        "    }\n",
        "\n",
        "  # 対照群の調整後のベクトル作成\n",
        "  df_0 <- filter(df, treat==0)\n",
        "  weighted_values0 <- vector()\n",
        "  for (i in 1:nrow(df_0)){\n",
        "    repeated_value0 <- df_0[[col]][i]\n",
        "    repeat_num0 <- round(df_0[[\"weights_multiplied\"]][i])\n",
        "    weighted_values0 <- append(weighted_values0, rep(repeated_value0, repeat_num0))\n",
        "    }\n",
        "\n",
        "  # 各群のサンプルサイズ\n",
        "  n1 <- length(weighted_values1)\n",
        "  n0 <- length(weighted_values0)\n",
        "\n",
        "  # 各群の平均値\n",
        "  m1 <- mean(weighted_values1)\n",
        "  m0 <- mean(weighted_values0)\n",
        "\n",
        "  # 各群の不偏分散\n",
        "  uv <- function(x){ var(x)*(length(x)-1)/length(x) }\n",
        "  v1 <- uv(weighted_values1)\n",
        "  v0 <- uv(weighted_values0)\n",
        "\n",
        "  # 効果量Hedgesのg\n",
        "  cov <- (v1*(n1-1) + v0*(n0-1)) / (n1+n0-2)\n",
        "  g <- (m1-m0) / sqrt(cov)\n",
        "\n",
        "  return (g)\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "qkrUFta-MqKt"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ASAMの検算\n",
        "cols_li <- c(\"age\", \"black\", \"hispanic\", \"married\", \"nodegree\", \"education\", \"re74\", \"re75\")\n",
        "for (col in cols_li){\n",
        "  print(paste(col, \"のASAM\"))\n",
        "  print(paste(\"調整前: \", standardized_mean_error(cps1_nsw_data, col)))\n",
        "  print(paste(\"調整後\", standardized_mean_error_weighted(cps1_nsw_data, \"weights\", col)))\n",
        "}\n",
        "\n",
        "# やっぱりlove.plotと結果が異なる\n",
        "# ただ増減の挙動は類似しているのでOK\n",
        "# マッチングと比べると調整はうまくいっていない"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi8FAeNcbQf_",
        "outputId": "f208e674-1716-413d-b5c6-770e4a97f81b"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"age のASAM\"\n",
            "[1] \"調整前:  -0.673045077618078\"\n",
            "[1] \"調整後 -0.645065994590819\"\n",
            "[1] \"black のASAM\"\n",
            "[1] \"調整前:  2.93324709421367\"\n",
            "[1] \"調整後 0.544955431150178\"\n",
            "[1] \"hispanic のASAM\"\n",
            "[1] \"調整前:  -0.0486883897274181\"\n",
            "[1] \"調整後 0.0970073665749104\"\n",
            "[1] \"married のASAM\"\n",
            "[1] \"調整前:  -1.1552824924244\"\n",
            "[1] \"調整後 -0.475543303472403\"\n",
            "[1] \"nodegree のASAM\"\n",
            "[1] \"調整前:  0.903320101340552\"\n",
            "[1] \"調整後 0.156113371826795\"\n",
            "[1] \"education のASAM\"\n",
            "[1] \"調整前:  -0.587471521697691\"\n",
            "[1] \"調整後 -0.242858375535186\"\n",
            "[1] \"re74 のASAM\"\n",
            "[1] \"調整前:  -1.25103406168653\"\n",
            "[1] \"調整後 -0.858869692212102\"\n",
            "[1] \"re75 のASAM\"\n",
            "[1] \"調整前:  -1.31388559386733\"\n",
            "[1] \"調整後 -0.786715907260075\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 回帰分析で効果を推定。(解析モデルにも共変量を使用)\n",
        "IPTW_result1 <- lm(data=cps1_nsw_data,\n",
        "formula = re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  weight = weighting1$weights) %>%\n",
        "    tidy()\n",
        "\n",
        "print(IPTW_result1)\n",
        "\n",
        "# 全然効果推定できていない\n",
        "# IPTWは介入グループと非介入グループの傾向が違う場合、分析結果が信頼しにくいことが知られている\n",
        "# 今回であれば、NSWとCPSのデータが混ざったような状況での効果推定となるため、NSWの実験を再現するようなアルゴリズムで無いため\n",
        "# cps1_nsw_dataの対照群はCPSのデータであり、収入が安定している個体が多い。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuSA5cwBU9zB",
        "outputId": "ef00ccbe-c13a-4610-c450-f3eeb64a6e40"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term         estimate std.error statistic   p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept) \u001b[4m1\u001b[24m\u001b[4m0\u001b[24m596.     483.        21.9   3.46\u001b[90me\u001b[39m\u001b[31m-105\u001b[39m\n",
            "\u001b[90m 2\u001b[39m treat       -\u001b[31m\u001b[4m1\u001b[24m50\u001b[39m\u001b[31m6\u001b[39m\u001b[31m.\u001b[39m     133.       -\u001b[31m11\u001b[39m\u001b[31m.\u001b[39m\u001b[31m3\u001b[39m   1.64\u001b[90me\u001b[39m\u001b[31m- 29\u001b[39m\n",
            "\u001b[90m 3\u001b[39m age           -\u001b[31m97\u001b[39m\u001b[31m.\u001b[39m\u001b[31m1\u001b[39m      6.46     -\u001b[31m15\u001b[39m\u001b[31m.\u001b[39m\u001b[31m0\u001b[39m   8.77\u001b[90me\u001b[39m\u001b[31m- 51\u001b[39m\n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m\u001b[4m3\u001b[24m16\u001b[39m\u001b[31m4\u001b[39m\u001b[31m.\u001b[39m     173.       -\u001b[31m18\u001b[39m\u001b[31m.\u001b[39m\u001b[31m3\u001b[39m   3.68\u001b[90me\u001b[39m\u001b[31m- 74\u001b[39m\n",
            "\u001b[90m 5\u001b[39m hispanic      -\u001b[31m78\u001b[39m\u001b[31m.\u001b[39m\u001b[31m7\u001b[39m    209.        -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m377\u001b[39m 7.06\u001b[90me\u001b[39m\u001b[31m-  1\u001b[39m\n",
            "\u001b[90m 6\u001b[39m married     -\u001b[31m\u001b[4m1\u001b[24m31\u001b[39m\u001b[31m3\u001b[39m\u001b[31m.\u001b[39m     136.        -\u001b[31m9\u001b[39m\u001b[31m.\u001b[39m\u001b[31m68\u001b[39m  4.27\u001b[90me\u001b[39m\u001b[31m- 22\u001b[39m\n",
            "\u001b[90m 7\u001b[39m nodegree    -\u001b[31m\u001b[4m1\u001b[24m81\u001b[39m\u001b[31m2\u001b[39m\u001b[31m.\u001b[39m     175.       -\u001b[31m10\u001b[39m\u001b[31m.\u001b[39m\u001b[31m4\u001b[39m   3.92\u001b[90me\u001b[39m\u001b[31m- 25\u001b[39m\n",
            "\u001b[90m 8\u001b[39m education     -\u001b[31m35\u001b[39m\u001b[31m.\u001b[39m\u001b[31m4\u001b[39m     31.9       -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m11\u001b[39m  2.67\u001b[90me\u001b[39m\u001b[31m-  1\u001b[39m\n",
            "\u001b[90m 9\u001b[39m re74            0.472    0.011\u001b[4m0\u001b[24m    43.0   0   \u001b[90m \u001b[39m    \n",
            "\u001b[90m10\u001b[39m re75            0.220    0.010\u001b[4m8\u001b[24m    20.4   2.54\u001b[90me\u001b[39m\u001b[31m- 91\u001b[39m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cps3_nsw_dataでも効果推定\n",
        "weghiting3 <- weightit(data=cps3_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  method=\"ps\",\n",
        "  estimand=\"ATE\")\n",
        "\n",
        "# 共変量のバランスを確認\n",
        "love.plot(weghiting3, threshold=0.1)\n",
        "\n",
        "IPW_result3 <- lm(data=cps3_nsw_data,\n",
        "formula=re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  weight=weghiting3$weights) %>%\n",
        "    tidy()\n",
        "\n",
        "print(IPW_result3)\n",
        "\n",
        "# cps1_nsw_data1に比べるとバランシングも推定結果もまともだが、\n",
        "# マッチングと比べると全然ダメ\n",
        "# 介入群と対照群の傾向の違いが影響していると思われる。"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "gRQnPusvvK7t",
        "outputId": "24c03599-1602-4ef1-94d4-b328f7dcc17a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Standardized mean differences and raw mean differences are present in the same plot. \n",
            "Use the `stars` argument to distinguish between them and appropriately label the x-axis.”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term        estimate std.error statistic   p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept) -\u001b[31m445\u001b[39m\u001b[31m.\u001b[39m    \u001b[4m2\u001b[24m530.        -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m176\u001b[39m 0.861    \n",
            "\u001b[90m 2\u001b[39m treat        713.     553.         1.29  0.198    \n",
            "\u001b[90m 3\u001b[39m age           22.7     33.3        0.682 0.495    \n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m959\u001b[39m\u001b[31m.\u001b[39m     610.        -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m57\u001b[39m  0.117    \n",
            "\u001b[90m 5\u001b[39m hispanic    -\u001b[31m352\u001b[39m\u001b[31m.\u001b[39m     911.        -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m386\u001b[39m 0.699    \n",
            "\u001b[90m 6\u001b[39m married     -\u001b[31m433\u001b[39m\u001b[31m.\u001b[39m     654.        -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m662\u001b[39m 0.508    \n",
            "\u001b[90m 7\u001b[39m nodegree     751.     835.         0.899 0.369    \n",
            "\u001b[90m 8\u001b[39m education    441.     168.         2.63  0.008\u001b[4m8\u001b[24m\u001b[4m6\u001b[24m  \n",
            "\u001b[90m 9\u001b[39m re74           0.197    0.058\u001b[4m9\u001b[24m     3.34  0.000\u001b[4m8\u001b[24m\u001b[4m7\u001b[24m\u001b[4m6\u001b[24m \n",
            "\u001b[90m10\u001b[39m re75           0.439    0.108      4.07  0.000\u001b[4m0\u001b[24m\u001b[4m5\u001b[24m\u001b[4m2\u001b[24m2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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OgExQ4AAEAnKHZwAp07dw4KCtI6hYhITEzMPffcc/nyZa2DAADgAMUOAABAJyh2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\nQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cps1_nsw_dataで効果推定\n",
        "# 対照群の分布を介入群に合わせにいくイメージ。なので重みは、介入群は1、対照群は(傾向スコア)/(1-(傾向スコア))。\n",
        "weghiting1_att <- weightit(data=cps1_nsw_data,\n",
        "formula = treat ~ age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  method=\"ps\",\n",
        "  estimand=\"ATT\")\n",
        "\n",
        "# 共変量のバランスを確認\n",
        "love.plot(weghiting1_att, threshold=0.1)\n",
        "\n",
        "IPW_result1_att <- lm(data=cps1_nsw_data,\n",
        "formula=re78 ~ treat + age + black + hispanic + married +\n",
        "  nodegree + education + re74 + re75,\n",
        "  weight=weghiting1_att$weights) %>%\n",
        "    tidy()\n",
        "\n",
        "print(IPW_result1_att)\n",
        "\n",
        "# バランシングはかなり良く、推定結果も大きく改善\n",
        "# やっぱりデータ自体の背景に合わせて、アルゴリズムを選ぶことが重要"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "JZPOTw8uwDvd",
        "outputId": "3f6550d4-0205-42ab-e7e5-2602a0c0c7c4"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“Some extreme weights were generated. Examine them with `summary()` and maybe trim them with `trim()`.”\n",
            "Warning message:\n",
            "“Standardized mean differences and raw mean differences are present in the same plot. \n",
            "Use the `stars` argument to distinguish between them and appropriately label the x-axis.”\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[90m# A tibble: 10 × 5\u001b[39m\n",
            "   term         estimate std.error statistic  p.value\n",
            "   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[90m 1\u001b[39m (Intercept)  \u001b[4m2\u001b[24m026.     523.        3.88   1.06\u001b[90me\u001b[39m\u001b[31m- 4\u001b[39m\n",
            "\u001b[90m 2\u001b[39m treat        \u001b[4m1\u001b[24m208.     107.       11.3    2.47\u001b[90me\u001b[39m\u001b[31m-29\u001b[39m\n",
            "\u001b[90m 3\u001b[39m age           -\u001b[31m29\u001b[39m\u001b[31m.\u001b[39m\u001b[31m2\u001b[39m      6.52     -\u001b[31m4\u001b[39m\u001b[31m.\u001b[39m\u001b[31m48\u001b[39m   7.65\u001b[90me\u001b[39m\u001b[31m- 6\u001b[39m\n",
            "\u001b[90m 4\u001b[39m black       -\u001b[31m\u001b[4m1\u001b[24m48\u001b[39m\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m     183.       -\u001b[31m8\u001b[39m\u001b[31m.\u001b[39m\u001b[31m11\u001b[39m   5.32\u001b[90me\u001b[39m\u001b[31m-16\u001b[39m\n",
            "\u001b[90m 5\u001b[39m hispanic      -\u001b[31m87\u001b[39m\u001b[31m.\u001b[39m\u001b[31m7\u001b[39m    281.       -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m312\u001b[39m  7.55\u001b[90me\u001b[39m\u001b[31m- 1\u001b[39m\n",
            "\u001b[90m 6\u001b[39m married         5.46   153.        0.035\u001b[4m7\u001b[24m 9.71\u001b[90me\u001b[39m\u001b[31m- 1\u001b[39m\n",
            "\u001b[90m 7\u001b[39m nodegree       22.5    169.        0.134  8.94\u001b[90me\u001b[39m\u001b[31m- 1\u001b[39m\n",
            "\u001b[90m 8\u001b[39m education     422.      32.3      13.1    9.62\u001b[90me\u001b[39m\u001b[31m-39\u001b[39m\n",
            "\u001b[90m 9\u001b[39m re74            0.101    0.016\u001b[4m1\u001b[24m    6.27   3.80\u001b[90me\u001b[39m\u001b[31m-10\u001b[39m\n",
            "\u001b[90m10\u001b[39m re75            0.346    0.023\u001b[4m2\u001b[24m   14.9    1.03\u001b[90me\u001b[39m\u001b[31m-49\u001b[39m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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ABAIQh2AAAACkGwAwDrkJCQ8Nxzz82a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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    }
  ]
}

## 02_basic_aggregation.ipynb

      
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## R_notebook.ipynb

      
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