Causal structure discovery from incomplete data
Name
1145169413-MIT.pdf
Size
1.36 MB
Format
Adobe PDF
Checksum (MD5)
d9c23e5c8c796cb81c0a5eefec77fa54
Author(s)
Squires, Chandler(Chandler B.)
Advisor(s)
Caroline Uhler.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Causal structure learning is a fundamental tool for building a scientific understanding of the way a system works. However, in many application areas, such as genomics, the information necessary for current causal structure learning algorithms does not match the information that researchers can actually access, for example when the algorithm requires knowledge of intervention targets but the interventions have off-target effects. In this thesis, we developed, implemented, and tested a novel algorithm for discovering a causal DAG from observational and interventional data, when the intervention targets are either partially or completely unknown. We relate the algorithm to the recently introduced Joint Causal Inference framework. Finally, we evaluate the performance of the algorithm on synthetic datasets and demonstrated its ability to outperform current state-of-the-art causal structure learning algorithms which assume known intervention targets.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 43-44).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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