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Paper

Detecting heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees Green, Donald Kern, Holger Uploaded 07-16-2010 Keywords causal inference heterogeneity ATE ensemble methods BART tree models MCMC Abstract We present a method that largely automates the search for systematic treatment effect heterogeneity in large-scale experiments. We introduce an estimator recently proposed in the statistical learning literature, Bayesian Additive Regression Trees (BART), to model treatment effects that vary as a function of covariates. BART has two important advantages over commonly employed parametric modeling strategies: it automates the search for treatment-covariate interactions and models them in a very flexible manner. To increase the reliability and credibility of the resulting conditional average treatment effect estimates, we suggest the use of a split sample analysis, which randomly divides the data into two equally-sized parts. The first part is used to search for systematic treatment effect heterogeneity; the second part is used to confirm the results. This approach permits a relatively unstructured exploration of systematic treatment effect heterogeneity while avoiding the pitfalls of data dredging and multiple comparisons. We illustrate the value of our approach by offering two empirical examples, a survey experiment on Americans' support for social welfare spending and a voter mobilization field experiment. In both applications, our approach provides robust insights into the nature and extent of systematic treatment effect heterogeneity.