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New Perspectives on Causal Mediation Analysis

  • Xiaolu Wang
  • Michael E. Sobel
Chapter
Part of the Handbooks of Sociology and Social Research book series (HSSR)

Abstract

Social and behavioral scientists have long used path analysis and related linear structural equation models (SEMs) to decompose parameters of ordered systems of equations into “direct effects” and “indirect effects” through mediating variables. These decompositions have been used to address substantive questions of fundamental interest, for example, how a person’s social background affects his/her earnings through education. However, in general, the “direct effects” and “indirect effects” defined in and estimated from these models should not be given causal interpretations, even in randomized experiments. To illustrate this, we first define various direct and indirect effects using potential outcomes notation and discuss situations where an investigator might want to consider these. Second, we consider identification of these effects: the required identifying assumptions are more often than not implausible for the kinds of data collected and questions considered in social and behavioral research. Third, we present other identifying assumptions that might be used to identify direct and indirect effects, and then briefly discuss different methods to estimate these effects, including regression, instrumental variables, marginal structural models, and weighting methods.Finally, we introduce an alternative approach to mediation (principal stratification), define several possible effects of interest, and briefly discuss identifying assumptions and estimation.

Keywords

Structural Equation Model Causal Model Control Effect Marginal Structural Model Causal Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Felix Elwert, Stephen Morgan, Geoffrey Wodtke, Kenneth Bollen and Judea Pearl for helpful comments. Any remaining errors are the authors’ alone.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Xiaolu Wang
    • 1
  • Michael E. Sobel
    • 2
  1. 1.Department of SociologyColumbia UniversityNew YorkUSA
  2. 2.Department of StatisticsColumbia UniversityNew YorkUSA

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