Cover: Weighting Estimators for Causal Mediation

Weighting Estimators for Causal Mediation

Published in: Handbook of Matching and Weighting Adjustments for Causal Inference, Chapter 19 (2023). doi: 10.1201/9781003102670

Posted on on August 11, 2023

by Donna L. Coffman, Megan S. Schuler, Trang Quynh Nguyen, Daniel F. McCaffrey

Mediation analysis involves questions about causal effects and mechanisms. Specifically, lesbian/gay and bisexual (LGB) women report higher rates of smoking and alcohol use than heterosexual women. Mediation is inherently about causal mechanisms, and causal effects are defined as the difference between two potential outcomes for an individual. Differences in estimates between the twang Mediation and causal weight packages can be attributed to differences between GBM and logistic estimation of weights. In terms of ease of use, the three methods require fitting different models (i.e., estimating different functions), so each may be more convenient in different settings. The Inverse Odds Ratio Weighting method involves the use of an odds ratio-based weight that relates the exposure and the mediator. There are different ways in which multiple mediators may be hypothesized to mediate the effect of an exposure on an outcome. Causal mediation analysis involves the comparison of groups with observed differences in their exposure and mediator status.

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