The Role of Placebo Samples in Observational Studies

TL;DR

This paper formalizes the use of placebo samples in observational studies to detect and correct bias, providing new estimation methods and demonstrating their effectiveness through simulations and real data.

Contribution

It introduces a formal framework and novel estimation techniques for leveraging placebo samples to identify and mitigate bias in observational research.

Findings

Placebo-based methods effectively detect unmeasured confounding.

Proposed estimators perform well in simulations.

Empirical application confirms practical utility.

Abstract

In an observational study, it is common to leverage known null effect 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 concern of unmeasured confounding bias while absence of it corroborates the causal conclusion. This paper establishes a formal framework for using a placebo sample to detect and remove bias. We state identification assumption, and develop estimation and inference methods based on outcome regression, inverse probability weighting, and doubly-robust approaches. Simulation studies and an empirical application illustrate the finite-sample performance of the proposed methods.