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

Jonathan M. Davis; Sara B. Heller

doi:10.1257/aer.p20171000

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

Jonathan M. Davis¹; Sara B. Heller²

¹ University of Chicago, 1129 E 59th Street, Chicago, IL 60637 (e-mail: ) · ² University of Pennsylvania, 3718 Locust Walk, McNeil Building 483, Philadelphia, PA 19104 (e-mail: )

American Economic Review 2017

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.

DOI: 10.1257/aer.p20171000
Volume: 107 (5)
Pages: 546-550
Language: en
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