Published August 17, 2023 | Version v1
Journal article Open

Placebo Tests for Causal Inference

  • 1. University of Chicago
  • 2. Princeton University
  • 3. DeepMind

Description

Placebo tests are increasingly common in applied social science research, but the methodological literature has not previously offered a comprehensive account of what we learn from them. We define placebo tests as tools for assessing the plausibility of the assumptions underlying a research design relative to some departure from those assumptions. We offer a typology of tests defined by the aspect of the research design that is altered to produce it (outcome, treatment, or population) and the type of assumption that is tested (bias assumptions or distributional assumptions). Our formal framework clarifies the extra assumptions necessary for informative placebo tests; these assumptions can be strong, and in some cases similar assumptions would justify a different procedure allowing the researcher to relax the research design's assumptions rather than test them. Properly designed and interpreted, placebo tests can be an important device for assessing the credibility of empirical research designs.

Data availability

The data and materials required to verify the computational reproducibility of the results, procedures, and analyses in this article are available on the American Journal of Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/3RR5RJ.

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Additional details

Identifiers

DOI
10.1111/ajps.12818
Other
oai:uchicago.tind.io:7447

Funding

Princeton University
School for Public and International Affairs

UChicago Information

Division(s)
Social Sciences Division
Department(s)
Political Science