Analyzing Causal Mechanisms in Survey Experiments. (3rd August 2018)

• Record Type: Journal Article
• Authors: Acharya, Avidit; Blackwell, Matthew; Sen, Maya
• Is Part Of: Political analysis. Volume 26:Number 4(2018)
• Page Start: 357
• Page End: 378
• Publication Date: 2018-08-03
• Subjects: causal inference, -- survey experiments, -- randomized experiments; Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1017/pan.2018.19
• Languages: English
• ISSNs: 1047-1987

Abstract

Abstract : 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.