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

Sara B. Heller & Jonathan M.V. Davis
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...
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