Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/cyberagentailab/dte-ml-adjustment

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

https://github.com/cyberagentailab/dte-ml-adjustment

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.9%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

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

Basic Info
  • Host: GitHub
  • Owner: CyberAgentAILab
  • License: mit
  • Language: R
  • Default Branch: main
  • Homepage:
  • Size: 4.99 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.md

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

Owner

  • Name: CyberAgent AI Lab
  • Login: CyberAgentAILab
  • Kind: organization
  • Location: Japan

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1