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Causal inference with observational data under cluster-specific non-ignorable assignment mechanism

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  • Kim, Gi-Soo
  • Paik, Myunghee Cho
  • Kim, Hongsoo

Abstract

An estimator of the population average causal treatment effect is proposed for multi-level clustered data from observational studies when the treatment assignment mechanism is cluster-specific non-ignorable. This is motivated from a health policy study to evaluate the cost associated with rehospitalization due to premature discharge. The proposed estimator utilizes cluster-level calibration condition and is shown to be consistent and asymptotically normal. The proposed method is evaluated along with existing methods through simulations and is applied to the health care cost study using California inpatient dataset.

Suggested Citation

  • Kim, Gi-Soo & Paik, Myunghee Cho & Kim, Hongsoo, 2017. "Causal inference with observational data under cluster-specific non-ignorable assignment mechanism," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 88-99.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:88-99
    DOI: 10.1016/j.csda.2016.10.002
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    References listed on IDEAS

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    1. Arpino, Bruno & Mealli, Fabrizia, 2011. "The specification of the propensity score in multilevel observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1770-1780, April.
    2. Ying Yuan & Roderick J. A. Little, 2007. "Model‐based estimates of the finite population mean for two‐stage cluster samples with unit non‐response," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(1), pages 79-97, January.
    3. Jae Kwang Kim & Yongchan Kwon & Myunghee Cho Paik, 2016. "Calibrated propensity score method for survey nonresponse in cluster sampling," Biometrika, Biometrika Trust, vol. 103(2), pages 461-473.
    4. Ying Yuan & Roderick J. A. Little, 2007. "Parametric and Semiparametric Model-Based Estimates of the Finite Population Mean for Two-Stage Cluster Samples with Item Nonresponse," Biometrics, The International Biometric Society, vol. 63(4), pages 1172-1180, December.
    5. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    6. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
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    Cited by:

    1. Jeong, Himchan & Valdez, Emiliano A., 2020. "Predictive compound risk models with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 182-195.
    2. Ranjbar, Setareh & Salvati, Nicola & Pacini, Barbara, 2023. "Estimating heterogeneous causal effects in observational studies using small area predictors," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).

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