Learning causal graphs under interventions and applications to single-cell biological data analysis
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1252627361-MIT.pdf
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735.39 KB
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Author(s)
Yang, Karren Dai.
Advisor(s)
Caroline Uhler and Douglas A. Lauffenburger.
Date Issued
2021
Publisher
Massachusetts Institute of Technology
Abstract
This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. This thesis first extends these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them, by defining and characterizing the interventional Markov equivalence class that can be identified from general interventions. Subsequently, this thesis proposes the first provably consistent algorithm for learning DAGs in this setting. Finally, this algorithm as well as related work is applied to analyze biological datasets.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 49-51).
Subjects
Biological Engineering.
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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