Mediation Analysis Is Harder Than It Looks

John G. Bullock, Shang E. Ha

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Mediators are variables that transmit causal effects from treatments to outcomes. Those who undertake mediation analysis seek to answer “how” questions about causation: how does this treatment affect that outcome? Typically, we desire answers of the form “the treatment affects a causally intermediate variable, which in turn affects the outcome.” Identifying these causally intermediate variables is the challenge of mediation analysis.

Conjectures about political mediation effects are as old as the study of politics. But codification of procedures by which to test hypotheses about mediation is a relatively new development. The most common procedures are now ubiquitous in psychology (Quiñones-Vidal et al. 2004) and increasingly popular in the other social sciences, not least political science.

Unfortunately, the most common procedures are not very good. They call for indirect effects – the portions of treatment effects that are transmitted through mediators – to be estimated via multiequation regression frameworks. These procedures do not require experimental manipulation of mediators; instead, they encourage the study of mediation with data from unmanipulated mediators (MacKinnon et al. 2002, 86; Spencer, Zanna, and Fong 2005). The procedures are therefore prone to producing biased estimates of mediation effects. Warnings about this problem have been issued for decades by statisticians, psychologists, and political scientists.
Original languageEnglish (US)
Title of host publicationCambridge Handbook of Experimental Political Science
EditorsJames N Druckman
PublisherCambridge University Press
Chapter35
Pages508-522
Number of pages15
ISBN (Print)978-0521174558
StatePublished - 2011

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