Degree Type

Thesis

Date of Award

2020

Degree Name

Master of Science

Department

Computer Science

Major

Computer Science

First Advisor

Jin Tian

Abstract

Confounding bias, missing data, and selection bias are three common obstacles to valid causal inference in the data sciences. Covariate adjustment is the most pervasive technique for recovering casual effects from confounding bias. In this thesis, we introduce a covariate adjustment formulation for controlling confounding bias in the presence of missing-not-at-random data and develop a necessary and sufficient condition for recovering causal effects using the adjustment. We also introduce an adjustment formulation for controlling both confounding and selection biases in the presence of missing data and develop a necessary and sufficient condition for valid adjustment. Furthermore, we present an algorithm that lists all valid adjustment sets and an algorithm that finds a valid adjustment set containing the minimum number of variables, which are useful for researchers interested in selecting adjustment sets with desired properties.

DOI

https://doi.org/10.31274/etd-20200902-132

Copyright Owner

Mojdeh Saadati

Language

en

File Format

application/pdf

File Size

50 pages

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