Design-Based Uncertainty for Quasi-Experiments

Journal of the American Statistical Association · 2026 · Volume 121 · Issue 553

DOI: 10.1080/01621459.2025.2526700

Abstract

Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities of receiving treatment, thereby allowing for rich forms of selection. We provide conditions under which the estimands of popular quasi-experimental estimators correspond to interpretable finite-population causal parameters. We characterize the biases and distortions to inference that arise when these conditions are violated. These results can be used to conduct sensitivity analyses when there are concerns about selection into treatment. Taken together, our results establish a rigorous foundation for quasi-experimental analyses that more closely aligns with the way empirical researchers discuss the variation in the data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Keywords

Causal inference, Finite population, Randomization inference, Selection into treatment

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