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Bayesian propensity score analysis for misclassified multinomial data

Author

Listed:
  • Yuhan Ma

    (Baylor University
    Scottish Rite for Children)

  • Joon Jin Song

    (Baylor University)

Abstract

A Bayesian propensity score analysis for misclassified multinomial outcome is proposed to estimate causal effects in observational studies. To cope with the non-identifiability issue in modeling misclassification, an informative Dirichlet prior is placed on the parameters of misclassification probabilities. The proposed method is compared with a naive method that ignores misclassification in a simulation study. The results show that the proposed method outperforms the naive method, with smaller biases and the coverage probabilities closer to the given nominal value. It is also found that ignoring misclassification in the outcome leads to biased estimation of the treatment effect. We apply the proposed model for a study on estimating the religious impact on the choice of contraception method.

Suggested Citation

  • Yuhan Ma & Joon Jin Song, 2025. "Bayesian propensity score analysis for misclassified multinomial data," Statistical Papers, Springer, vol. 66(5), pages 1-17, August.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:5:d:10.1007_s00362-025-01724-8
    DOI: 10.1007/s00362-025-01724-8
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    References listed on IDEAS

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    2. Carlos Daniel Paulino & Paulo Soares & John Neuhaus, 2003. "Binomial Regression with Misclassification," Biometrics, The International Biometric Society, vol. 59(3), pages 670-675, September.
    3. Corwin M. Zigler & Krista Watts & Robert W. Yeh & Yun Wang & Brent A. Coull & Francesca Dominici, 2013. "Model Feedback in Bayesian Propensity Score Estimation," Biometrics, The International Biometric Society, vol. 69(1), pages 263-273, March.
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    5. Qi Zhou & Yoo-Mi Chin & James D. Stamey & Joon Jin Song, 2018. "Bayesian misclassification and propensity score methods for clustered observational studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1547-1560, July.
    6. Kosuke Imai & Teppei Yamamoto, 2010. "Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 543-560, April.
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