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Treatment Effects: A Bayesian Perspective

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  • James J. Heckman
  • Hedibert F. Lopes
  • Rémi Piatek

Abstract

This paper contributes to the emerging Bayesian literature on treatment effects. It derives treatment parameters in the framework of a potential outcomes model with a treatment choice equation, where the correlation between the unobservable components of the model is driven by a low-dimensional vector of latent factors. The analyst is assumed to have access to a set of measurements generated by the latent factors. This approach has attractive features from both theoretical and practical points of view. Not only does it address the fundamental identification problem arising from the inability to observe the same person in both the treated and untreated states, but it also turns out to be straightforward to implement. Formulae are provided to compute mean treatment effects as well as their distributional versions. A Monte Carlo simulation study is carried out to illustrate how the methodology can easily be applied.

Suggested Citation

  • James J. Heckman & Hedibert F. Lopes & Rémi Piatek, 2014. "Treatment Effects: A Bayesian Perspective," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 36-67, June.
    • Handle: RePEc:taf:emetrv:v:33:y:2014:i:1-4:p:36-67
    DOI: 10.1080/07474938.2013.807103
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    References listed on IDEAS

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    1. James Heckman & Rodrigo Pinto & Peter Savelyev, 2013. "Understanding the Mechanisms through Which an Influential Early Childhood Program Boosted Adult Outcomes," American Economic Review, American Economic Association, vol. 103(6), pages 2052-2086, October.
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    Cited by:

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    2. Watanabe, Hajime & Maruyama, Takuya, 2024. "A Bayesian sample selection model with a binary outcome for handling residential self-selection in individual car ownership," Journal of choice modelling, Elsevier, vol. 51(C).
    3. Barnard, Helena & Cowan, Robin & Müller, Moritz, 2016. "On the value of foreign PhDs in the developing world: Training versus selection effects," Working Paper Series in Economics 82, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    4. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    5. Horvath, Akos & Lang, Peter, 2021. "Do loan subsidies boost the real activity of small firms?," Journal of Banking & Finance, Elsevier, vol. 122(C).
    6. Liana Jacobi & Helga Wagner & Sylvia Frühwirth-Schnatter, 2014. "Bayesian Treatment Effects Models with Variable Selection for Panel Outcomes with an Application to Earnings Effects of Maternity Leave," NRN working papers 2014-12, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    7. Marie Djuikom, 2018. "Incentives to labour migration and agricultural productivity: The Bayesian perspective," WIDER Working Paper Series 45, World Institute for Development Economic Research (UNU-WIDER).
    8. Fitzenberger, Bernd & Osikominu, Aderonke & Paul, Marie, 2023. "The effects of training incidence and planned training duration on labor market transitions," Journal of Econometrics, Elsevier, vol. 235(1), pages 256-279.
    9. Müller, Moritz & Cowan, Robin & Barnard, Helena, 2018. "On the value of foreign PhDs in the developing world: Training versus selection effects in the case of South Africa," Research Policy, Elsevier, vol. 47(5), pages 886-900.
    10. Marie Albertine Djuikom, 2018. "Incentives to labour migration and agricultural productivity: The Bayesian perspective," WIDER Working Paper Series wp-2018-45, World Institute for Development Economic Research (UNU-WIDER).
    11. Murat K. Munkin, 2022. "Count Roy model with finite mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1160-1181, September.
    12. Shimeles Abebe & Andinet Woldemichael, 2015. "Working Paper 225 - Measuring the Impact of Micro-Health Insurance on Healthcare Utilization: A Bayesian Potential Outcomes Approach," Working Paper Series 2166, African Development Bank.
    13. Rémi Piatek & Pia Pinger, 2016. "Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 734-755, June.
    14. Jacobi, Liana & Wagner, Helga & Frühwirth-Schnatter, Sylvia, 2016. "Bayesian treatment effects models with variable selection for panel outcomes with an application to earnings effects of maternity leave," Journal of Econometrics, Elsevier, vol. 193(1), pages 234-250.
    15. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2022. "Double Robust Bayesian Inference on Average Treatment Effects," Papers 2211.16298, arXiv.org, revised Oct 2024.

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