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Causal decomposition analysis aims to identify risk factors (referred to as “mediators”) that contribute to social disparities in an outcome. Despite promising developments in causal decomposition analysis, current methods are limited to addressing a time-fixed mediator and outcome only, which has restricted our understanding of the causal mechanisms underlying social disparities. In particular, existing approaches largely overlook individual characteristics when designing (hypothetical) interventions to reduce disparities. To address this issue, we extend current longitudinal mediation approaches to the context of disparities research. Specifically, we develop a novel decomposition analysis method that addresses individual characteristics by (a) using optimal dynamic treatment regimes (DTRs) and (b) conditioning on a selective set of individual characteristics. Incorporating optimal DTRs into the design of interventions can be used to strike a balance between equity (reducing disparities) and excellence (improving individuals’ outcomes). We illustrate the proposed method using the High School Longitudinal Study data.
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Choosing an Optimal Method for Causal Decomposition Analysis with Continuous Outcomes: A Review and Simulation Study
Causal decomposition analysis is among the rapidly growing number of tools for identifying factors (“mediators”) that contribute to disparities in outcomes between social groups. An example of such mediators is college completion, which explains later health disparities between Black women and White men. The goal is to quantify how much a disparity would be reduced (or remain) if we hypothetically intervened to set the mediator distribution equal across social groups. Despite increasing interest in estimating disparity reduction and the disparity that remains, various estimation procedures are not straightforward, and researchers have scant guidance for choosing an optimal method. In this article, the authors evaluate the performance in terms of bias, variance, and coverage of three approaches that use different modeling strategies: (1) regression-based methods that impose restrictive modeling assumptions (e.g., linearity) and (2) weighting-based and (3) imputation-based methods that rely on the observed distribution of variables. The authors find a trade-off between the modeling assumptions required in the method and its performance. In terms of performance, regression-based methods operate best as long as the restrictive assumption of linearity is met. Methods relying on mediator models without imposing any modeling assumptions are sensitive to the ratio of the group-mediator association to the mediator-outcome association. These results highlight the importance of selecting an appropriate estimation procedure considering the data at hand.
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- Award ID(s):
- 1749275
- PAR ID:
- 10517229
- Publisher / Repository:
- Sage
- Date Published:
- Journal Name:
- Sociological Methodology
- Volume:
- 54
- Issue:
- 1
- ISSN:
- 0081-1750
- Page Range / eLocation ID:
- 92 to 117
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/00811750231183711
