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Marginal Treatment Effects from a Propensity Score Perspective

Author

Listed:
  • Xiang Zhou
  • Yu Xie

Abstract

We offer a propensity score perspective to interpret and analyze the marginal treatment effect (MTE). Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score and a latent variable representing unobserved resistance to treatment. As with the original MTE, the redefined MTE can be used as a building block for constructing standard causal estimands. The weights associated with the new MTE, however, are simpler, more intuitive, and easier to compute. Moreover, the redefined MTE immediately reveals treatment effect heterogeneity among individuals at the margin of treatment, enabling us to evaluate a wide range of policy effects.

Suggested Citation

  • Xiang Zhou & Yu Xie, 2019. "Marginal Treatment Effects from a Propensity Score Perspective," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 3070-3084.
    • Handle: RePEc:ucp:jpolec:doi:10.1086/702172
    DOI: 10.1086/702172
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    Cited by:

    1. Julian Martinez-Iriarte & YiXiao Sun, 2022. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," Working Papers 131, Red Nacional de Investigadores en Economía (RedNIE).
    2. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.
    3. Laura Schmitz, 2022. "Heterogeneous Effects of After-School Care on Child Development," Discussion Papers of DIW Berlin 2006, DIW Berlin, German Institute for Economic Research.
    4. Elena Ashtari Tafti, 2022. "Technology, skills, and performance: the case of robots in surgery," IFS Working Papers W22/46, Institute for Fiscal Studies.
    5. Martínez-Iriarte, Julian & Sun, Yixiao, 2021. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," University of California at San Diego, Economics Working Paper Series qt2bc57830, Department of Economics, UC San Diego.
    6. Bedi, Shaibu Mellon & Azzarri, Carlo & Kotu, Bekele Hundi & Kornher, Lukas, 2021. "Scaling-up Agricultural Innovations: Who Should be Targeted?," 2021 Conference, August 17-31, 2021, Virtual 315267, International Association of Agricultural Economists.
    7. Spanos, Yiannis E., 2021. "Exploring heterogeneous returns to collaborative R&D: A marginal treatment effects perspective," Research Policy, Elsevier, vol. 50(5).
    8. Hoshino Tadao & Yanagi Takahide, 2022. "Estimating marginal treatment effects under unobserved group heterogeneity," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 197-216, January.
    9. Tafti, Elena Ashtari, 2023. "Technology, Skills, and Performance: The Case of Robots in Surgery," CINCH Working Paper Series (since 2020) 78746, Duisburg-Essen University Library, DuEPublico.
    10. Shaibu Mellon Bedi & Carlo Azzarri & Bekele Hundie Kotu & Lukas Kornher & Joachim von Braun, 2022. "Scaling-up agricultural technologies: who should be targeted?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 857-875.
    11. Zhewen Pan & Zhengxin Wang & Junsen Zhang & Yahong Zhou, 2024. "Marginal treatment effects in the absence of instrumental variables," Papers 2401.17595, arXiv.org, revised Aug 2024.
    12. Kim Huynh & Robert Petrunia & Joel Rodrigue & Walter Steingress, 2023. "Exporting and Investment Under Credit Constraints," Staff Working Papers 23-10, Bank of Canada.
    13. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.

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