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In observational studies, the existence of confounding variables should be attended to, and propensity score weighting methods are often used to eliminate their effects. Although many causal estimators have been proposed based on propensity scores, these estimators generally assume that the propensity scores are properly estimated. However, researchers have found that even a slight misspecification of the propensity score model can result in a bias of estimated treatment effects. Model misspecification problems may occur in practice, and hence, using a robust estimator for causal effect is recommended. One such estimator is a subclassification estimator. Wang, Zhang, Richardson, & Zhou (2020) presented the conditions necessary for subclassification estimators to have √ N-consistency and to be asymptotically well-defined and suggested an idea how to construct subclasses.

Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification