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Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap

Author

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  • Christoph Rothe

Abstract

Limited overlap between the covariate distributions of groups with different treatment assignments does not only make estimates of average treatment effects rather imprecise, but can also lead to substantially distorted confidence intervals. This paper argues that this is because the coverage error of traditional confidence intervals is driven by the number of observations in the areas of limited overlap. Some of these “local sample sizes” can be very small in applications, up to the point that distributional approximations derived from classical asymptotic theory become unreliable. Building on this observation, this paper constructs confidence intervals based on classical approaches to small sample inference. The approach is easy to implement, and has superior theoretical and practical properties relative to standard methods in empirically relevant settings.

Suggested Citation

  • Christoph Rothe, 2017. "Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap," Econometrica, Econometric Society, vol. 85, pages 645-660, March.
    • Handle: RePEc:wly:emetrp:v:85:y:2017:i::p:645-660
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    Cited by:

    1. Heiler, Phillip & Kazak, Ekaterina, 2021. "Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores," Journal of Econometrics, Elsevier, vol. 222(2), pages 1083-1108.
    2. Mengqi Li, 2025. "Fragility in Average Treatment Effect on the Treated under Limited Covariate Support," Papers 2506.08950, arXiv.org.
    3. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org.
    4. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    5. Gerhard Riener & Sebastian Schneider & Valentin Wagner, 2020. "Addressing Validity and Generalizability Concerns in Field Experiments," Discussion Paper Series of the Max Planck Institute for Behavioral Economics 2020_16, Max Planck Institute for Behavioral Economics.
    6. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    7. Soonwoo Kwon & Liyang Sun, 2025. "Estimating Treatment Effects Under Bounded Heterogeneity," Papers 2510.05454, arXiv.org.
    8. Taisuke Otsu & Mengshan Xu, 2022. "Isotonic propensity score matching," STICERD - Econometrics Paper Series 623, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    9. Tsakiridis, Andreas & O’Donoghue, Cathal & Ryan, Mary & Cullen, Paula & Ó hUallacháin, Daire & Sheridan, Helen & Stout, Jane, 2022. "Examining the relationship between farmer participation in an agri-environment scheme and the quantity and quality of semi-natural habitats on Irish farms," Land Use Policy, Elsevier, vol. 120(C).
    10. Bernhard Schmidpeter, 2015. "The Fatal Consequences of Grief," Economics working papers 2015-06, Department of Economics, Johannes Kepler University Linz, Austria.
    11. repec:jku:cdlwps:2015_07 is not listed on IDEAS
    12. Timothy B. Armstrong & Michal Kolesár, 2021. "Finite‐Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness," Econometrica, Econometric Society, vol. 89(3), pages 1141-1177, May.
    13. Bernhard Schmidpeter, 2015. "The Fatal Consequences of Grief," CDL Aging, Health, Labor working papers 2015-07, The Christian Doppler (CD) Laboratory Aging, Health, and the Labor Market, Johannes Kepler University Linz, Austria.
    14. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    15. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    16. Jacob Dorn, 2025. "How Much Weak Overlap Can Doubly Robust T-Statistics Handle?," Papers 2504.13273, arXiv.org, revised Apr 2025.
    17. Sokbae Lee & Martin Weidner, 2021. "Bounding Treatment Effects by Pooling Limited Information across Observations," Papers 2111.05243, arXiv.org, revised May 2025.
    18. Mengshan Xu & Taisuke Otsu, 2022. "Isotonic propensity score matching," Papers 2207.08868, arXiv.org, revised Jan 2025.
    19. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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