The experimentdatar data package contains publicly available datasets that were used in Susan Athey and Guido Imbens’ course “Machine Learning and Econometrics” (AEA continuing Education, 2018). The datasets are conveniently packed for R users.

Installation

You can install the development version from GitHub

Usage

Load experimentdatar and other required libraries

library(dplyr)
library(experimentdatar)

Load the social dataset and display the response and treatment variables

## # A tibble: 6 x 2
##   outcome_voted treat_neighbors
##           <int>           <int>
## 1             0               0
## 2             1               0
## 3             1               0
## 4             0               0
## 5             0               0
## 6             0               0

There is also a function dataDetails() that opens the original paper where the data was used

dataDetails("social")

List of available datasets

  • charitable: Data used for the paper “Does Price matter in charitable giving? Evidence from a large-Scale Natural Field experiment”
    by Karlan and List (2007).

  • IVdataset: Data used for the paper “Does compulsory school attendance affect schooling and earnings?”
    by Angrist and Krueger (1991) and related papers.

  • mobilization: Data for the paper “Comparing Experimental and Matching Methods Using a Large-Scale Voter Mobilization Experiment”
    by Arceneaux, Gerber, and Green (2006).

  • vouchers: Data for the paper “Vouchers for Private Schooling in Colombia: Evidence from a Randomized Natural Experiment”
    by Angrist, Bettinger, Bloom, King, and Kremer (2002).

  • secrecy: Data for the paper “Ballot Secrecy Concerns and Voter Mobilization: New Experimental Evidence about Message Source, Context, and the Duration of Mobilization Effects”
    by Gerber, Hubers, Biggers, and Hendry (2014).

  • social: Data for the paper “Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment”
    by Gerber, Green, and Larimer (2008).

  • welfare: Data for the paper “Modeling heterogeneous treatment effects in survey experiments with Bayesian Additive Regression Trees”
    by Green and Kern (2012).

Code of conduct

Please note that the ‘experimentdatar’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.