Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes

TL;DR

This paper systematically clarifies the assumptions needed for causal mediation effect identification, providing a clear framework and practical guidance for researchers to conduct and interpret mediation analyses accurately.

Contribution

It introduces a structured approach to understanding and selecting assumptions for identifying various causal mediation effects, improving clarity and practical application.

Findings

Weaker assumptions suffice for certain effect identification.

Identification assumptions vary across different causal contrasts.

Practical implications for positivity assumptions in mediation analysis.

Abstract

Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This paper provides a systematic explanation of such assumptions. We define five potential outcome types whose means are involved in various effect definitions. We tackle their mean/distribution's identification, starting with the one that requires the weakest assumptions and gradually building up to the one that requires the strongest assumptions. This presentation shows clearly why an assumption is required for one estimand and not another, and provides a succinct table from which an applied researcher could pick out the assumptions required for identifying the causal effects they target. Using a running example, the paper illustrates the assembling and consideration of identifying assumptions for a range of causal contrasts. For several that are commonly encountered in the literature, this exercise clarifies that identification requires weaker assumptions than those often stated in the literature. This attention to the details also draws attention to the differences in the positivity assumption for different estimands, with practical implications. Clarity on the identifying assumptions of these various estimands will help researchers conduct appropriate mediation analyses and interpret the results with appropriate caution given the plausibility of the assumptions.