A Review and Synthesis of Multi-level Models for Causal Inference with Individual Level Exposures

Purpose of review Multi-level models are ways to model data using multiple levels of information. Here, we provide a narrative review some of the relevant literature on how multi-level models can interface with causal inference for individual level exposures. Recent findings Much of this discussion focuses on clarifying and synthesizing some of the complex ideas in the literature. We discuss how multi-level models can be seen as approximate ways to relax some of the identifying conditions of causal inference. Summary There are significant gaps in the literature on causal inference with multi-level models, but we list some published approaches for further guidance. We close with some practical advice on when multi-level models might be best utilized for causal inference and how they might be used in ways that go beyond simply interpreting their (potentially highly conditional) parameters.