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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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    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.

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    More about this item

    Keywords

    SUTVA; Bounds; Average treatment effect; Sensitivity analysis;
    All these keywords.

    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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