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A Bayesian Nonparametric Approach for Causal Inference with Missing Covariates

Zang, Huaiyu
http://orcid.org/0000-0001-8550-3827
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583247173001619

Abstract Details

2020, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Missing covariates in observational studies are common. Inappropriately handling missing data on covariates could have an impact on the causal effect estimation. Causal analysis on complete-case records could result in inefficiency due to a loss in sample size as well as potentially biased causal estimation. Besides missing data problems, the complexity of data structure makes the causal inference more difficult. In real data, the data distribution could be very complex; a standard parametric model lacks its flexibility. To address these problems, we introduce a Bayesian nonparametric causal model to estimate causal effects with missing covariates, that simultaneously imputes missing values and estimates causal effects under a potential outcome framework. We compare the performance of our method to complete-case analyses and two-step approaches (the sequential-chain imputation followed by the off-the-shelf causal inference methods) via repeated sampling simulations. Our simulation results show that our method produces accurate average treatment effect estimates as well as good imputation performance to preserve joint distributions of complicated data. In the simulation studies, we confirm that a bad imputation model could negatively impact the causal estimation and learn that we need to choose a good imputation model and check imputation performance for correct causal inference analysis. The proposed method is also applied to Juvenile Idiopathic Arthritis data, extracted from electronic medical records, comparing effectiveness of early aggressive use of biological medication in treating children.
Hang Joon Kim, Ph.D. (Committee Chair)
Bin Huang, Ph.D. (Committee Member)
Siva Sivaganesan, Ph.D. (Committee Member)
Xia Wang, Ph.D. (Committee Member)
Nanhua Zhang, Ph.D. (Committee Member)
85 p.
Statistics
Bayesian nonparametrics; Causal effect; Missing covariates; Multiple Imputation; Observational studies

Recommended Citations

Citations

  • Zang, H. (2020). A Bayesian Nonparametric Approach for Causal Inference with Missing Covariates [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583247173001619

    APA Style (7th edition)

  • Zang, Huaiyu. A Bayesian Nonparametric Approach for Causal Inference with Missing Covariates. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583247173001619.

    MLA Style (8th edition)

  • Zang, Huaiyu. "A Bayesian Nonparametric Approach for Causal Inference with Missing Covariates." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583247173001619

    Chicago Manual of Style (17th edition)

ucin1583247173001619
86
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.