Design-Based Uncertainty for Quasi-Experiments

Ashesh Rambachan and Jonathan Roth

Papers from arXiv.org

Abstract: Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This paper develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment is at least partially determined by idiosyncratic factors but there are concerns about endogenous 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.

Date: 2020-08, Revised 2024-10
New Economics Papers: this item is included in nep-ecm and nep-exp
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