Uthpala Wanigasekara , Ph.D. Candidate , Department of Biostatistics and Bioinformatics, University of Louisville

"Bayesian methods for causal inference and propensity scores"

This project focuses on Bayesian joint and two-stage methods for propensity score analysis, which is a statistical method used to account for confounding variables in observational studies. Propensity score modeling involves calculating each patient’s propensity score and using the obtained values to design a regression model. The Bayesian joint propensity score methods provide feedback between the stages, which means the results of the model are fed back into the model. We propose a likelihood framework to optimize the parameter tau, which represents the average treatment effect. The proposed likelihood structure involves a normal distribution for the response variable given the treatment and the covariates in the outcome model and a Bernoulli distribution for the treatment given the covariates in the propensity score model. The coefficients of the covariates in the propensity score model and the outcome model are estimated through the optimization process. We will demonstrate the proposed method with simulation studies. The results of this project will contribute to the understanding of the effectiveness of Bayesian joint and two-stage methods for propensity score analysis and provide insights into the potential biases that may arise from feedback in joint Bayesian PS estimation.

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