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Bayesian Analysis of Treatment Effects in an Ordered Potential Outcomes Model

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  • Li, Mingliang
  • Tobias, Justin

Abstract

We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely {\it data augmentation}, the {\it Gibbs sampler} and the {\it Metropolis-Hastings algorithm}, can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Several computational strategies which allow for non-Normality are also discussed. Conventional ``treatment effects'' such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are derived for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects.

Suggested Citation

  • Li, Mingliang & Tobias, Justin, 2008. "Bayesian Analysis of Treatment Effects in an Ordered Potential Outcomes Model," Staff General Research Papers Archive 12429, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12429
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    Cited by:

    1. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    2. Munkin, Murat K. & Trivedi, Pravin K., 2008. "Bayesian analysis of the ordered probit model with endogenous selection," Journal of Econometrics, Elsevier, vol. 143(2), pages 334-348, April.
    3. Andinet Woldemichael & Daniel Gurara & Abebe Shimeles, 2019. "The Impact of Community Based Health Insurance Schemes on Out-of-Pocket Healthcare Spending: Evidence from Rwanda," IMF Working Papers 2019/038, International Monetary Fund.
    4. Andrés Ramírez–Hassan & Rosember Guerra–Urzola, 2021. "Bayesian treatment effects due to a subsidized health program: the case of preventive health care utilization in Medellín (Colombia)," Empirical Economics, Springer, vol. 60(3), pages 1477-1506, March.
    5. Woldemichael, Andinet & Gurara, Daniel Zerfu & Shimeles, Abebe, 2016. "Community-Based Health Insurance and Out-of-Pocket Healthcare Spending in Africa: Evidence from Rwanda," IZA Discussion Papers 9922, Institute of Labor Economics (IZA).
    6. 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.
    7. Steven T. Yen & Donald J. Bruce & Lisa Jahns, 2012. "Supplemental Nutrition Assistance Program Participation And Health: Evidence From Low‐Income Individuals In Tennessee," Contemporary Economic Policy, Western Economic Association International, vol. 30(1), pages 1-12, January.
    8. Li, Mingliang & Mumford, Kevin J. & Tobias, Justin L., 2012. "A Bayesian analysis of payday loans and their regulation," Journal of Econometrics, Elsevier, vol. 171(2), pages 205-216.

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