Hierarchical Causal Models

Analyzing nested data with hierarchical models is a staple of Bayesian statistics, but causal modeling remains largely focused on “flat” models. In this talk, we will explore how to think about nested data in causal models, and we will consider the advantages of nested data over aggregate data (such as means) for causal inference. We show that disaggregating your data replacing a flat causal model with a hierarchical causal model can provide new opportunities for identification and estimation. As examples, we will study how to identify and estimate causal effects under unmeasured confounders, interference, and instruments.