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Analyzing Causal Mechanisms in Survey Experiments

  • Avidit Acharya (a1), Matthew Blackwell (a2) and Maya Sen (a3)
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.

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Contributing Editor: R. Michael Alvarez

Many thanks to John Ahlquist, Josh Kertzer, Ryan T. Moore, Paul Testa, and Teppei Yamamoto for helpful feedback. Special thanks to Jessica Weeks and Mike Tomz for sharing their survey instrument with us. Replication data and code can be found in Acharya, Blackwell, and Sen (2018).

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Acharya, Avidit, Blackwell, Matthew, and Sen, Maya. 2016. Explaining causal findings without bias: detecting and assessing direct effects. American Political Science Review 110(3):512529.
Acharya, Avidit, Blackwell, Matthew, and Sen, Maya. 2018. Replication data for: analyzing causal mechanisms in survey experiments. doi:10.7910/DVN/KHE44F, Harvard Dataverse, V1, UNF:6:VaeMnsesFmBVwg98eK/heA==.
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
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