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Abstract

Hierarchical models play three important roles in modeling causal effects: (i) accounting for data collection, such as in stratified and split-plot experimental designs; (ii) adjusting for unmeasured covariates, such as in panel studies; and (iii) capturing treatment effect variation, such as in subgroup analyses. Across all three areas, hierarchical models, especially Bayesian hierarchical modeling, offer substantial benefits over classical, non-hierarchical approaches. After discussing each of these topics, we explore some recent developments in the use of hierarchical models for causal inference and conclude with some thoughts on new directions for this research area.

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