Analyzing Causal Mechanisms in Survey Experiments

Researchers investigating causal mechanisms in survey experiments often rely on nonrandomized quantities to isolate the indirect effect of treatment through these variables. Such an approach, however, requires a “selection-on-observables” assumption, which undermines the advantages of a randomized e...

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Bibliographic Details
Published in:Political analysis 2018-10, Vol.26 (4), p.357-378
Main Authors: Acharya, Avidit, Blackwell, Matthew, Sen, Maya
Format: Article
Language:English
Subjects:
Causality
Democratic peace theory
Election results
Experiments
Identification
Mediation
Nominations
Partisanship
Polls & surveys
Researchers
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Summary:Researchers investigating causal mechanisms in survey experiments often rely on nonrandomized quantities to isolate the indirect effect of treatment through these variables. Such an approach, however, requires a “selection-on-observables” assumption, which undermines the advantages of a randomized experiment. In this paper, we show what can be learned about casual mechanisms through experimental design alone. We propose a factorial design that provides or withholds information on mediating variables and allows for the identification of the overall average treatment effect and the controlled direct effect of treatment fixing a potential mediator. While this design cannot identify indirect effects on its own, it avoids making the selection-on-observable assumption of the standard mediation approach while providing evidence for a broader understanding of causal mechanisms that encompasses both indirect effects and interactions. We illustrate these approaches via two examples: one on evaluations of US Supreme Court nominees and the other on perceptions of the democratic peace.
ISSN:1047-1987
1476-4989
DOI:10.1017/pan.2018.19