CyberAgentAILab / dte-ml-adjustment

Code for "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction"

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction

This repository contains code to replicate the experimental results from "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction."

Folders

1. data folder includes files to create dataset used for empirical application from Ferraro & Price (2013). Download original data from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN1/22633&version=1.1 and save 090113_TotWatDat_cor_merge_Price.dta file in data folder.

2. experiment folder contains all R files used for analysis

Experiment Files

1. functions.R file includes all necessary functions

2. run_simulation.R includes code to run the Monte Carlo simulations and saves results as .rds files

3. compute_stats.R includes code to calculate evaluation metrics (e.g. bias, RMSE) from the saved simulation results (.rds files) and saves them as .csv files

4. plot_figures.R includes code to load the .csv files and plot figures for the simulation study

5. experiment_water_consumption.R includes code to replicate the analysis of experimental data from Ferraro & Price (2013)

Instructions

1. Install all necessary packages in R
2. To replicate the results from the Monte Carlo simulation, run the files in the following order: (1) run_simulation.R, (2) compute_stats.R, (3) plot_figures.R. The outputs will be figures appeared in Figures 1, 3 and 4 in the paper.
3. Run experiment_water_consumption.R to replicate the results from the water consumption experiment. The output will be figures appeared in Figure 2 in the paper.

R version and attached packages

• R version 4.3.1

• RColorBrewer_1.1-3 ggpubr_0.6.0 fastglm_0.0.3 bigmemory_4.6.1 xgboost_1.7.5.1 foreign_0.8-84 ggplot2_3.4.3 dplyr_1.1.2 doParallel_1.0.17 glmnet_4.1-8 Matrix_1.6-1.1 doMC_1.3.8 iterators_1.0.14 foreach_1.5.2 grf_2.3.1 randomForest_4.7-1.1 gridExtra_2.3 tidyr_1.3.0 haven_2.5.3 readr_2.1.4