2026-09-19T06:48:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1242632026-06-06T00:54:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Caroline Uhler.
Squires, Chandler(Chandler B.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-03-24T15:36:59Z
2020-03-24T15:36:59Z
2019
2019
https://hdl.handle.net/1721.1/124263
1145169413
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).
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.
by Chandler Squires.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
44 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Causal structure discovery from incomplete data
Thesis
application/pdf
2020-03-24T15:36:57Z
Publication
Master
EECS
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6469211a-ccbb-4cdf-80da-25c0f6825013">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Causal structure discovery from incomplete data</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2019</PublicationDate>
<Authors>
<Author>
<DisplayName>Squires, Chandler(Chandler B.)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>