# Recursive partitioning for heterogeneous causal effects

@article{Athey2016RecursivePF, title={Recursive partitioning for heterogeneous causal effects}, author={Susan Athey and Guido Imbens}, journal={Proceedings of the National Academy of Sciences}, year={2016}, volume={113}, pages={7353 - 7360} }

In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates…

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