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Partial identification of functionals of the joint distribution of “potential outcomes”

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  • Fan, Yanqin
  • Guerre, Emmanuel
  • Zhu, Dongming

Abstract

In this paper, we present a systematic study of partial identification of two general classes of functionals of the joint distribution of two “potential outcomes” when a bivariate sample from the joint distribution is not available to the econometrician. Assuming the identification of the conditional marginal distributions of potential outcomes and the distribution of the covariate vector, we show that the identified sets for functionals in both classes are intervals and provide conditions under which the identified sets point identify the true value of the functionals. In addition, we establish sufficient and necessary conditions for the covariate information to be informative in the sense of shrinking the identified sets. We focus on the application of our general results to evaluating distributional treatment effects of a binary treatment in two commonly used frameworks in the literature for evaluating average treatment effects: the selection on observables framework and a latent threshold-crossing model. We characterize the role of the propensity score in the selection-on-observables framework and the role of endogenous selection in the latent threshold-crossing model. Examples of policy parameters that our results apply include the correlation coefficient between the potential outcomes, many inequality measures of the distribution of treatment effects, and median of the distribution of the individual treatment effect.

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  • Fan, Yanqin & Guerre, Emmanuel & Zhu, Dongming, 2017. "Partial identification of functionals of the joint distribution of “potential outcomes”," Journal of Econometrics, Elsevier, vol. 197(1), pages 42-59.
    • Handle: RePEc:eee:econom:v:197:y:2017:i:1:p:42-59
    DOI: 10.1016/j.jeconom.2016.10.005
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    9. Takahiro Hoshino & Keisuke Takahata, 2018. "Identification of heterogeneous treatment effects as a function of potential untreated outcome under the nonignorable assignment condition," Keio-IES Discussion Paper Series 2018-005, Institute for Economics Studies, Keio University.
    10. John Mullahy, 2017. "Individual Results May Vary: Elementary Analytics of Inequality-Probability Bounds, with Applications to Health-Outcome Treatment Effects," NBER Working Papers 23603, National Bureau of Economic Research, Inc.
    11. Christian Dudel & Julian Schmied, 2019. "Pension adequacy standards: an empirical estimation strategy and results for the United States and Germany," MPIDR Working Papers WP-2019-003, Max Planck Institute for Demographic Research, Rostock, Germany.
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    16. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org.
    17. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
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    More about this item

    Keywords

    Copula; Distributional treatment effect; Selection-on-observables; Latent threshold-crossing model;
    All these keywords.

    JEL classification:

    • 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
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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