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Theses/Dissertations

Statistics and Probability

2023

Causal Inference

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Causal Inference For The Effect Of Continuous Treatment On Time-To-Event Outcomes And Mediation Analysis On Health Disparities In Observational Studies., Triparna Poddar Dec 2023

Causal Inference For The Effect Of Continuous Treatment On Time-To-Event Outcomes And Mediation Analysis On Health Disparities In Observational Studies., Triparna Poddar

Electronic Theses and Dissertations

The dissertation comprises two projects related to causal inference based on observational data. In healthcare research, where abundant observational data such as claims data and electronic records are available, researchers often aim to study the treatment effect and the pathway of that effect. However, estimating treatment effects in observational data presents challenges due to confounding factors. The first project focuses on estimating continuous treatment effects for survival outcomes, while the second concentrates on mediation analysis, allowing the exploration of the pathway of the causal effect. Both projects involve addressing confounding variables. In the first project, I investigate estimation of the …


High Dimensional Data Analysis: Variable Screening And Inference, Lei Fang Jan 2023

High Dimensional Data Analysis: Variable Screening And Inference, Lei Fang

Theses and Dissertations--Statistics

This dissertation focuses on the problem of high dimensional data analysis, which arises in many fields including genomics, finance, and social sciences. In such settings, the number of features or variables is much larger than the number of observations, posing significant challenges to traditional statistical methods.

To address these challenges, this dissertation proposes novel methods for variable screening and inference. The first part of the dissertation focuses on variable screening, which aims to identify a subset of important variables that are strongly associated with the response variable. Specifically, we propose a robust nonparametric screening method to effectively select the predictors …