Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs
Jonathan M.V. Davis, Sara B. Heller
American Economic Review · 2017 · Volume 107 · Issue 5
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.
Keywords
Single Equation Models, Single Variables: Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions, Fertility, Family Planning, Child Care, Children, Youth, Mobility, Unemployment, and Vacancies: Public Policy
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