Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs
Jonathan Davis () and
Sara B. Heller
American Economic Review, 2017, vol. 107, issue 5, 546-50
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.
JEL-codes: C21 J13 J68 (search for similar items in EconPapers)
Note: DOI: 10.1257/aer.p20171000
References: Add references at CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
https://www.aeaweb.org/articles/attachments?retrie ... U7y1NCLDcWxHTqOpxzMZ (application/zip)
https://www.aeaweb.org/articles/attachments?retrie ... EHj9kCQtUlf7f-N2VSUI (application/pdf)
https://www.aeaweb.org/articles/attachments?retrie ... S6mJ3Le0dzkm8KC9JHSQ (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aea:aecrev:v:107:y:2017:i:5:p:546-50
Ordering information: This journal article can be ordered from
Access Statistics for this article
American Economic Review is currently edited by Esther Duflo
More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().