Date on Master's Thesis/Doctoral Dissertation

12-2020

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Bioinformatics and Biostatistics

Degree Program

Biostatistics, PhD

Committee Chair

Kong, Maiying

Committee Co-Chair (if applicable)

Pal, Subhadip

Committee Member

Duncan, Scott

Committee Member

Gaskins, Jeremy

Committee Member

Zheng, Qi

Author's Keywords

Bayesian; causal inference; propensity score; selection bias; machine learning

Abstract

This dissertation consists of three projects related to Modified-Half-Normal distribution and causal inference. In my first project, a new distribution called Modified-Half-Normal distribution was introduced. I explored a few of its distributional properties, the procedures for generating random samples based on Bayesian approaches, and the parameter estimation based on the method of moments. The second project deals with the problem of selection bias of average treatment effect (ATE) if we use the observational data. I combined the propensity score based inverse probability of treatment weighting (IPTW) method and the directed acyclic graph (DAG) to solve this problem. The third project solves the problem of bias ATE using observational data by combining the doubly robust methods with the super learner algorithm.

Download

Share

COinS