Replication data for: Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs

Principal Investigator(s): View help for Principal Investigator(s) Jonathan M.V. Davis; Sara B. Heller

Name	File Type	Size	Last Modified
Data			10/20/2021 01:28:PM
LICENSE.txt	text/plain	14.6 KB	10/12/2019 07:16:AM

Project Citation:

Davis, Jonathan M.V., and Heller, Sara B. Replication data for: Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs. Nashville, TN: American Economic Association [publisher], 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113487V1

Project Description

Summary: To estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, we implement Wager and Athey's (2015) causal forest algorithm. We provide a step-by-step explanation targeted at applied researchers of how the algorithm predicts treatment effects based on observables. We then explore how useful the predicted heterogeneity is in practice by testing whether youth with larger predicted treatment effects actually respond more in a hold-out sample. Our application highlights some limitations of the causal forest, but it also suggests that the method can identify treatment heterogeneity for some outcomes that more standard interaction approaches would have missed.

Scope of Project

Subject Terms: Heterogenous Treatment Effects; Summer Jobs; Randomized Controlled Trial; Machine Learning

JEL Classification:
      C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
      J13 Fertility; Family Planning; Child Care; Children; Youth
      J68 Mobility, Unemployment, and Vacancies: Public Policy

Geographic Coverage: Cook County, Illinois

Time Period(s): 5/1/2012 – 7/1/2015 (Summers 2012 and 2013 through 2 years post random assignment)

Universe: Applicants to 2012 and 2013 One Summer Plus Programs (2013 program only served males, so female applicants are dropped)

Data Type(s): administrative records data; experimental data

Methodology

Data Source: Data was provided by Chicago's Department of Family and Support Services, the Illinois State Police, Chicago Public. Schools, and the Illinois Department of Employment Security.

Unit(s) of Observation: Individual,

Related Publications

Download this project

Published Versions

V1 [2019-10-12]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.