Causal Inference Using Mediation Analysis or Instrumental Variables - Full Mediation in the Absence of Conditional Independence
Marketing ZFP – Journal of Research and Management, 40(2): 41-57, 2018
24 Pages Posted: 6 Mar 2018 Last revised: 2 Apr 2020
Date Written: March 4, 2018
Both instrumental variable (IV) estimation and mediation analysis are tools for causal inference. However, IV estimation has mostly developed in economics for causal inference from observational data. In contrast, mediation analysis has mostly developed in psychology, as a tool to empirically establish the process by which an experimental manipulation brings about its effect on the dependent variable of interest. As a consequence, many researchers well versed in IV estimation are not familiar with mediation analysis, and vice versa. In this paper, we discuss the communalities and differences between IV estimation and mediation analysis. We highlight that IV estimation leverages an a priori assumption of full mediation for causal inference. In contrast, modern practice in mediation analysis focusses on testing the statistical significance of the indirect effect without too much regard for the specification of the estimated model. A drawback of this approach is that inferring mediation from the statistical significance of a (putative) indirect effect through the hypothesized mediator may be spurious altogether. We discuss specification issues and how they relate to inference about mediation, and specifically the distinction between full and partial mediation. Based on this discussion we argue in favor of further developing tests that are more diagnostic about the underlying causal structure, motivated by the implication that full mediation could be more common than currently believed.
Keywords: Mediation, Instrumental Variables, Causal Inference, Observational Equivalence, Bayesian Model Comparison
JEL Classification: C11, C12, C26, C52
Suggested Citation: Suggested Citation