Testing Complex Social Theories With Causal Mediation Analysis and G-Computation: Toward a Better Way to Do Causal Structural Equation Modeling

Krisztián Pósch

Sociological Methods & Research, 2021, vol. 50, issue 3, 1376-1406

Abstract: Complex social scientific theories are conventionally tested using linear structural equation modeling (SEM). However, the underlying assumptions of linear SEM often prove unrealistic, making the decomposition of direct and indirect effects problematic. Recent advancements in causal mediation analysis can help to address these shortcomings, allowing for causal inference when a new set of identifying assumptions are satisfied. This article reviews how these ideas can be generalized to multiple mediators, with a focus on the posttreatment confounding and causal ordering cases. Using the potential outcome framework as a rigorous tool for causal inference, the application is the theory of procedural justice policing. Analysis of data from two randomized experiments shows that making similar parametric assumptions to SEMs and using G-computation improve the viability of effect decomposition. The article concludes with a discussion of how causal mediation analysis improves upon SEM and the potential limitation of the methods.

Keywords: causal mediation analysis; causal ordering; structural equation modeling; G-computation; causal inference; police legitimacy; potential outcome framework; posttreatment confounding; procedural justice policing; sensitivity analysis (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:50:y:2021:i:3:p:1376-1406

DOI: 10.1177/0049124119826159

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