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

# %% # obs lalonde data from Kline paper - init housekeeping
libreq(data.table, fixest, rio)
cps3 = import("cps3re74.dta") %>% setDT() %>% na.omit()
setnames(cps3, c("re78", "treat"), c("y", "W"));
xs = setdiff(colnames(cps3), c("y", 'W'))
W = cps3$W  %>% as.matrix(); Y = cps3$y  %>% as.matrix()
X = cbind(1, cps3[, ..xs]) %>% as.matrix()
X1 = X[W==1,]; X0 = X[W==0,]
N = length(W); N_t = sum(W)
# %% first way - KOB / kline - page 1

apoorvalal

library(CVXR); library(data.table)

# %% functions
# reshape panel data from long to wide for factor models / outcomes
panelMatrices = function(dt, unit_id, time_id, treat, outcome) {
  dt = as.data.table(dt)
  # function to extract first column, convert it to rownames for a matrix
  matfy = function(X) {
    idnames = as.character(X[[1]])
    X2 = as.matrix(X[, -1])

apoorvalal

#' Compute Neyman allocation propensity scores for inference-optimal treatment assignment in data table [very fast]
#' @param df data.table
#' @param y outcome name
#' @param w treatment name
#' @param x covariate names (must all be discrete)
#' @return data.table with strata level conditional means, variances, propensity scores,
#'  and neyman allocation propensities.
#' @export
neymanAllocation = function(df, y, w, x){
    df1 = copy(df); N = nrow(df1)