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Identification of Treatment Effects Under Conditional Partial Independence

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  • Matthew A. Masten
  • Alexandre Poirier

Abstract

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.

Suggested Citation

  • Matthew A. Masten & Alexandre Poirier, 2018. "Identification of Treatment Effects Under Conditional Partial Independence," Econometrica, Econometric Society, vol. 86(1), pages 317-351, January.
    • Handle: RePEc:wly:emetrp:v:86:y:2018:i:1:p:317-351
    DOI: 10.3982/ECTA14481
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    Cited by:

    1. Tenglong Li & Kenneth A. Frank, 2020. "The probability of a robust inference for internal validity and its applications in regression models," Papers 2005.12784, arXiv.org.
    2. Mourifié, Ismael & Wan, Yuanyuan, 2025. "Layered policy analysis in program evaluation using the marginal treatment effect," Journal of Econometrics, Elsevier, vol. 251(C).
    3. Arkadiusz Szydłowski, 2019. "Endogenous censoring in the mixed proportional hazard model with an application to optimal unemployment insurance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1086-1101, November.
    4. Pedro Picchetti, 2025. "Breakdown Analysis for Instrumental Variables with Binary Outcomes," Papers 2507.10242, arXiv.org, revised Oct 2025.
    5. Zequn Jin & Gaoqian Xu & Xi Zheng & Yahong Zhou, 2025. "Policy Learning under Unobserved Confounding: A Robust and Efficient Approach," Papers 2507.20550, arXiv.org.
    6. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    7. Matthew A. Masten & Alexandre Poirier, 2022. "The Effect of Omitted Variables on the Sign of Regression Coefficients," Papers 2208.00552, arXiv.org, revised Jun 2025.
    8. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
    9. Tenglong Li & Kenneth A. Frank & Mingming Chen, 2024. "A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study," Mathematics, MDPI, vol. 12(3), pages 1-14, January.
    10. Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2022. "Assessing Omitted Variable Bias when the Controls are Endogenous," Papers 2206.02303, arXiv.org, revised Feb 2026.
    11. Sakaue, Katsuki & Wokadala, James, 2022. "Effects of including refugees in local government schools on pupils’ learning achievement: Evidence from West Nile, Uganda," International Journal of Educational Development, Elsevier, vol. 90(C).
    12. Yiwei Sun, 2023. "Extrapolating Away from the Cutoff in Regression Discontinuity Designs," Papers 2311.18136, arXiv.org.
    13. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    14. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    15. Christophe Bruneel-Zupanc, 2023. "Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs," Papers 2303.12667, arXiv.org, revised Sep 2025.
    16. Nathan Canen & Kyungchul Song, 2021. "Counterfactual analysis under partial identification using locally robust refinement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 416-436, June.
    17. Chinh Hoang-Duc & Hang Nguyen-Thu & Tuan Nguyen-Anh & Hiep Tran-Duc & Linh Nguyen-Thi-Thuy & Phuong Do-Hoang & Nguyen To-The & Vuong Vu-Tien & Huong Nguyen-Thi-Lan, 2024. "Governmental support and multidimensional poverty alleviation: efficiency assessment in rural areas of Vietnam," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 22(4), pages 999-1038, December.
    18. Pedro Picchetti, 2026. "Sensitivity Analysis for Instrumental Variables Under Joint Relaxations of Monotonicity and Independence," Papers 2603.25529, arXiv.org, revised Mar 2026.
    19. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.

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