Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs
- (pp. 546-50)
AbstractTo 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.
CitationDavis, Jonathan M.V., and Sara B. Heller. 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs." American Economic Review, 107 (5): 546-50. DOI: 10.1257/aer.p20171000
- 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