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• W2885403954 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." @default.
• W2885403954 created "2018-08-22" @default.
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• W2885403954 date "2018-08-03" @default.
• W2885403954 modified "2023-10-17" @default.
• W2885403954 title "Analyzing Causal Mechanisms in Survey Experiments" @default.
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• W2885403954 doi "https://doi.org/10.1017/pan.2018.19" @default.
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