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Identification of the average treatment effect when SUTVA is violated

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Abstract

The stable unit treatment value assumption (SUTVA) ensures that only two potential outcomes exist and that one of them is observed for each individual. After providing new insights on SUTVA validity, we derive sharp bounds on the average treatment effect (ATE) of a binary treatment on a binary outcome as a function of the share of units, a, for which SUTVA is potentially violated. Then we show how to compute the maximum value of a such that the sign of the ATE is still identified. After decomposing SUTVA into two separate assumptions, we provide weaker conditions that might help sharpening our bounds. Furthermore, we show how some of our results can be extended to continuous outcomes. Finally, we estimate our bounds in two well known experiments, the U.S. Job Corps training program and the Colombian PACES vouchers for private schooling.

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  • Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.
    • Handle: RePEc:hhs:sdueko:2020_003
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    References listed on IDEAS

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    1. Demuynck, Thomas, 2015. "Bounding average treatment effects: A linear programming approach," Economics Letters, Elsevier, vol. 137(C), pages 75-77.
    2. Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects from Spillover, Interaction, and General Equilibrium Effects," IZA Discussion Papers 10648, Institute of Labor Economics (IZA).
    3. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    4. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    5. Joshua Angrist & Eric Bettinger & Michael Kremer, 2006. "Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia," American Economic Review, American Economic Association, vol. 96(3), pages 847-862, June.
    6. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    7. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    8. Keisuke Hirano & Jack R. Porter, 2012. "Impossibility Results for Nondifferentiable Functionals," Econometrica, Econometric Society, vol. 80(4), pages 1769-1790, July.
    9. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    10. Martin Huber & Andreas Steinmayr, 2021. "A Framework for Separating Individual-Level Treatment Effects From Spillover Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 422-436, March.
    11. Hiroaki Kaido & Francesca Molinari & Jörg Stoye, 2019. "Confidence Intervals for Projections of Partially Identified Parameters," Econometrica, Econometric Society, vol. 87(4), pages 1397-1432, July.
    12. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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    Cited by:

    1. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    2. Lafférs, Lukáš & Nedela, Roman, 2025. "Sensitivity of Bounds on ATEs under Survey Nonresponse," Econometrics and Statistics, Elsevier, vol. 34(C), pages 1-13.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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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