arXiv 9 Nov 2021 · Econometrics · publishedJournal of Econometrics (2026)
arXiv:2111.05243 · PDF · DOI · OpenAlex · Extracted main text
We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through two empirical application that our bounds are indeed robust and informative in practice.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Crump, R. K., V. J. Hotz, G. W. Imbens, and O. A. Mitnik (2009) Dealing with limited overlap in estimation of average treatment effects | 0.874 | 6 | 2 | 100% |
| 2 | Connors, Alfred F., J., T. Speroff, N. V. Dawson, C. Thomas, J. Harr… (1996) The Effectiveness of Right Heart Catheterization in the Initial Care of Critically ill Patients | 0.874 | 5 | 2 | 100% |
| 3 | Armstrong, T. B. and M. Kolesár (2021) Finite-sample optimal estimation and inference on average treatment effects under unconfoundedness | 0.811 | 4 | 2 | 100% |
| 4 | Li, F., K. L. Morgan, and A. M. Zaslavsky (2018) Balancing covariates via propensity score weighting | 0.811 | 4 | 2 | 100% |
| 5 | Rothe, C (2017) Robust confidence intervals for average treatment effects under limited overlap | 0.811 | 4 | 2 | 100% |
| 6 | Manski, C. F (1989) Anatomy of the selection problem | 0.811 | 4 | 2 | 100% |
| 7 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.811 | 4 | 2 | 100% |
| 8 | Stoye, J (2020) A simple, short, but never-empty confidence interval for partially identified parameters | 0.644 | 4 | 1 | 100% |
| 9 | Dehejia, R. H. and S. Wahba (1999, December) (1999) Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs | 0.644 | 2 | 2 | 100% |
| 10 | Ma, X., Y. Sasaki, and Y. Wang (2025) Testing limited overlap | 0.585 | 3 | 1 | 100% |
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