关键词：
Binary potential outcomes, causal inference, maximum likelihood tree, propensity scores
摘要：
Observational studies of relatively large data can have poten- tially hidden heterogeneity with respect to causal effects and propensity scores–patterns of a putative cause being exposed to study subjects. This underlying heterogeneity can be crucial in causal inference for any obser- vational studies because it is systematically generated and structured by covariates which influence the cause and/or its related outcomes. Address- ing the causal inference problem in view of data structure, machine learning techniques such as tree analysis can be naturally necessitated. Kang, Su, Hitsman, Liu and Lloyd-Jones (2012) proposed Marginal Tree (MT) proce- dure to explore both the confounding and interacting effects of the covariates on causal inference. In this paper, we extend the MT method to the case of binary responses along with a clear exposition of its relationship with estab- lished causal odds ratio. We assess the causal effect of dieting on emotional distress using both a real data set from the Lalonde’s National Supported Work Demonstration Analysis (NSW) and a simulated data set from the National Longitudinal Study of Adolescent Health (Add Health).
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