Causal Structure Learning: A Combinatorial Perspective
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10208_2022_Article_9581.pdf
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829.39 KB
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Adobe PDF
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bb640a76ee7a989f26a888f37acbaefd
Author(s)
Squires, Chandler
Uhler, Caroline
Date Issued
August 1, 2022
Publisher
Springer US
Citation
Squires, Chandler and Uhler, Caroline. 2022. "Causal Structure Learning: A Combinatorial Perspective."
Version
Final published version
Abstract
Abstract
In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs and various generalizations which allow for some variables to be unobserved in the available data. We devote special attention to two fundamental combinatorial aspects of causal structure learning. First, we discuss the structure of the search space over causal graphs. Second, we discuss the structure of equivalence classes over causal graphs, i.e., sets of graphs which represent what can be learned from observational data alone, and how these equivalence classes can be refined by adding interventional data.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10208-022-09581-9
