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Facilitating Score And Causal Inference Trees For Large Observational Studies, Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A. Levine, Xin Yan

Faculty Bibliography 2010s

Assessing treatment effects in observational studies is a multifaceted problem that not only involves heterogeneous mechanisms of how the treatment or cause is exposed to subjects, known as propensity, but also differential causal effects across sub-populations. We introduce a concept termed the facilitating score to account for both the confounding and interacting impacts of covariates on the treatment effect. Several approaches for estimating the facilitating score are discussed. In particular, we put forward a machine learning method, called causal inference tree (CIT), to provide a piecewise constant approximation of the facilitating score. With interpretable rules, CIT splits data in such …