My da|ra Login

Detailed view

metadata language: English

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

Version
V0
Resource Type
Dataset
Creator
  • Davis, Jonathan M.V.
  • Heller, Sara B.
Publication Date
2017-05-01
Description
  • Abstract

    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.
Availability
Download
Relations
  • Is supplement to
    DOI: 10.1257/aer.p20171000 (Text)
Publications
  • Davis, Jonathan M.V., and Sara B. Heller. “Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs.” American Economic Review 107, no. 5 (May 2017): 546–50. https://doi.org/10.1257/aer.p20171000.
    • ID: 10.1257/aer.p20171000 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12

Davis, Jonathan M.V.; Heller, Sara B. (2017): Replication data for: Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E113487