Role of placebo samples in observational studies

Ye Ting (), He Qijia, Chen Shuxiao and Zhang Bo
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Ye Ting: Department of Biostatistics, University of Washington, Washington, United States
He Qijia: Department of Statistics, University of Washington, Washington, United States
Chen Shuxiao: Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States
Zhang Bo: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States

Journal of Causal Inference, 2025, vol. 13, issue 1, 12

Abstract: In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample – a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo sample raises concerns about unmeasured confounding bias while the absence of it helps corroborate the causal conclusion. This article describes a framework for using a placebo sample to detect and remove bias. We state the identification assumptions and develop estimation and inference methods based on outcome regression, inverse probability weighting, and doubly robust approaches. Simulation studies investigate the finite-sample performance of the proposed methods. We illustrate the methods using an empirical study of the effect of the earned income tax credit on infant health.

Keywords: causal inference; placebo test; treatment effect heterogeneity; unmeasured confounding (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:13:y:2025:i:1:p:12:n:1001

DOI: 10.1515/jci-2023-0020

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