Placebo Tests for Causal Inference

Andrew C. Eggers; Guadalupe Tuñón; Allan Dafoe

doi:10.1111/ajps.12818

journal article Open Access Aug 17, 2023

Placebo Tests for Causal Inference

Andrew C. Eggers Guadalupe Tuñón Allan Dafoe

American Journal of Political Science Vol. 68 No. 3 pp. 1106-1121 · JSTOR

View at Publisher Save 10.1111/ajps.12818

Abstract

AbstractPlacebo 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.

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References

43

[1]

10.1086/686631

[2]

10.1515/9781400888740

[3]

10.1097/ede.0000000000000504

[4]

10.1086/688736

[5]

Cattaneo Matias D. (2020)

[6]

10.1111/j.1540-5907.2012.00613.x

[7]

10.1111/rssb.12348

[8]

10.2307/2344179

[9]

10.1136/bmj.a2533

[10]

10.1111/ajps.12285

[11]

10.1017/s0020818317000236

[12]

10.1177/014107689909201005

[13]

10.1257/aer.20180805

[14]

10.1162/003355397555190

[15]

10.1017/s0003055413000178

[16]

10.1017/cbo9781139084444

[17]

Enos Ryan D "Can violent protest change local policy support? evidence from the aftermath of the 1992 Los Angeles riot" American Political Science Review (2017)

[18]

10.1017/s0003055411000141

[19]

10.1017/s0003055416000241

[20]

10.1086/681563

[21]

10.1086/699244

[22]

10.1511/2014.111.460

[23]

10.1198/000313002605

[24]

10.1016/s0895-4356(03)00177-x

[25]

10.1111/ajps.12433

[26]

10.1111/ajps.12387

[27]

10.1093/pan/mps021

[28]

10.1201/9781003226055

[29]

10.1017/cbo9781139025751

[30]

10.1017/s0003055413000464

[31]

10.1097/ede.0b013e3181d61eeb

[32]

10.1017/9781108233590

[33]

10.1111/ajps.12355

[34]

10.1111/1475-6773.12355

[35]

Protzko John.2018. “Null‐Hacking a Lurking Problem in the Open Science Movement.”PsyArXiv. 10.31234/osf.io/9y3mp

[36]

10.1080/01621459.1984.10477060

[37]

10.1007/978-1-4757-3692-2

[38]

10.2307/2531497

[39]

10.1111/j.2517-6161.1983.tb01242.x

[40]

Royall Richard. (1997)

[41]

10.1002/sim.5925

[42]

10.1146/annurev.polisci.11.060606.135444

[43]

10.1017/s0003055414000173

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Details

Published: Aug 17, 2023
Vol/Issue: 68(3)
Pages: 1106-1121
License: View

Authors

A

Andrew C. Eggers

University of Chicago

Princeton University

DeepMind and Centre for the Governance of AI

Cite This Article

Andrew C. Eggers, Guadalupe Tuñón, Allan Dafoe (2023). Placebo Tests for Causal Inference. American Journal of Political Science, 68(3), 1106-1121. https://doi.org/10.1111/ajps.12818

Placebo Tests for Causal Inference

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