Ashesh Rambachan, Jonathan Roth
arXiv 3 Aug 2020 · Econometrics · publishedJournal of the American Statistical Association (2025) · 8 citations (OpenAlex)
arXiv:2008.00602 · PDF · DOI · OpenAlex · Extracted main text
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 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wherry and Miller (2016) Early Coverage, Access, Utilization, and Health Effects Associated With the Affordable Care Act Medicaid Expansions | 1.000 | 5 | 3 | 100% |
| 2 | Abadie, Athey, Imbens and Wooldridge (2020) Sampling-Based versus Design-Based Uncertainty in Regression Analysis | 0.874 | 8 | 2 | 100% |
| 3 | Neyman (1923) On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 | 0.874 | 6 | 2 | 100% |
| 4 | Li and Ding (2017) General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference | 0.843 | 4 | 3 | 75% |
| 5 | Freedman (2008) On Regression Adjustments to Experimental Data | 0.843 | 3 | 3 | 100% |
| 6 | Lin (2013) Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman's Critique | 0.843 | 3 | 3 | 100% |
| 7 | Abadie, Athey, Imbens and Wooldridge (2023) When Should You Adjust Standard Errors for Clustering? | 0.811 | 4 | 2 | 100% |
| 8 | Hajek (1964) Asymptotic Theory of Rejective Sampling with Varying Probabilities from a Finite Population | 0.763 | 9 | 5 | 44% |
| 9 | Imbens and Manski (2004) Confidence Intervals for Partially Identified Parameters | 0.737 | 15 | 4 | 40% |
| 10 | Rambachan and Roth (2023) A More Credible Approach to Parallel Trends | 0.737 | 4 | 3 | 50% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.