apoorvalal

from joblib import Parallel, delayed
import numpy as np
import pandas as pd


class LinearMediation:
    def __init__(self):
        pass

    def fit(self, X, W, y, store=True):

apoorvalal

\documentclass[%
  article,
  % beamer,
  beameroptions={ignorenonframetext,14pt},
  articleoptions={a4paper,12pt},
  also={trans,handout,article}
  ]{beamerswitch}
\handoutlayout{nup=3plus,border=1pt}
\articlelayout{maketitle,frametitles=none}
\mode<article>{\usepackage[hmargin=2cm,vmargin=2cm]{geometry}}

apoorvalal

import numpy as np
import pandas as pd
from scipy.stats import norm
from sklearn.model_selection import cross_val_predict, KFold

# learners
from xgboost import XGBRegressor
from glum import GeneralizedLinearRegressorCV
from sklearn.kernel_ridge import KernelRidge

apoorvalal

library(estimatr)
data(auto)
# %% FWL regression coefficient
auto$ytil = lm(price ~ displacement, auto)$resid
auto$x2til = lm(weight ~ displacement, auto)$resid
(fwlest = lm_robust(ytil ~ x2til, auto, se_type = "HC0")
  %>% summary %>% .$coefficients %>% .[2, 1:2])
# %%
(fullest =
  lm_robust(price ~ weight + displacement, auto, se_type = "HC0") %>%

apoorvalal

import numpy as np
from scipy.linalg import lstsq
np.random.seed(42)
# %%
def ols(X, y, vcov = 'HC1', driver = 'gelsy'):
    """
    Fast, minimal implementation of least squares regression with robust SEs

    Args:
        X:          n X p array of covariates

apoorvalal

# %%
pacman::p_load(knitr, tidyverse, DeclareDesign, glmnet)
set.seed(42)

# %% estimator functions
p_hacker = function(data) {
  fit_1 = lm_robust(Y ~ Z + X1, data = data)
  fit_3 = lm_robust(Y ~ Z + X1 + X2, data = data)
  fit_2 = lm_robust(Y ~ Z + X2 + X3 + X4, data = data)
  fit_4 = lm_robust(Y ~ Z + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = data)

apoorvalal

pacman::p_load(synthdid, ebal, glue, augsynth, MCPanel, glue)
# needs https://github.com/apoorvalal/ebal - solves ebal problem in torch - far more stable than old version
# remotes::install_github("apoorvalal/ebal")

# %% simulator for panel balancing
#' @param n number of units
#' @param t number of time periods
#' @param parallel_trends boolean for parallel trends
#' @param random_assignment boolean for random assignment of treatment
#' @param σ noise level in mapping from factor to outcome

apoorvalal

# For documentation, see http://www.sumatrapdfreader.org/settings3.2.html

MainWindowBackground = #80fff200
EscToExit = false
ReuseInstance = false
UseSysColors = false
RestoreSession = true

FixedPageUI [
	TextColor = #ffffff

apoorvalal

rm(list = ls())
libreq(data.table, fixest, rio, ggplot2, ebal)



# %%
cps3 = import("cps3re74.dta") |> setDT()
cps3 = cps3 |> na.omit()
setnames(cps3, c("re78", "treat"), c("y", "d"))
xs = c("age", "age2", "ed", "black", "hisp", "married", "nodeg", "re74", "re75")

apoorvalal