Degree Type
Creative Component
Semester of Graduation
Summer 2020
Department
Computer Science
First Major Professor
Jin Tian
Degree(s)
Master of Science (MS)
Major(s)
Computer Science
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.
Copyright Owner
Mojdeh Saadati
Copyright Year
2020
File Format
Recommended Citation
Saadati, Mojdeh, "Adjustment criteria for recovering causal effects from missing data" (2020). Creative Components. 694.
https://lib.dr.iastate.edu/creativecomponents/694
1
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Other Computer Sciences Commons, Theory and Algorithms Commons
