Open this publication in new window or tab >>2023 (English)In: SIAM Journal on Discrete Mathematics, ISSN 0895-4801, E-ISSN 1095-7146, Vol. 37, no 1, p. 233-252Article in journal (Refereed) Published
Abstract [en]
Finding a directed acyclic graph (DAG) that best encodes the conditional inde-pendence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example, the greedy equivalence search, greedy interventional equivalence search, and max-min hill climbing algorithms. In 2010, Studenty, Hemmecke, and Lindner introduced the char-acteristic imset (CIM) polytope, CIMp, whose vertices correspond to Markov equivalence classes, as a way of transforming causal discovery into a linear optimization problem. We show that the moves of the aforementioned algorithms are included within classes of edges of CIMp and that restrictions placed on the skeleton of the candidate DAGs correspond to faces of CIMp. Thus, we observe that greedy equivalence search, greedy interventional equivalence search, and max-min hill climbing all have geometric realizations as greedy edge-walks along CIMp. Furthermore, the identified edges of CIMp strictly generalize the moves of these algorithms. Exploiting this generalization, we introduce a greedy simplex-type algorithm called greedy CIM, and a hybrid variant, skeletal greedy CIM, that outperforms current competitors among hybrid and constraint-based algorithms.
Place, publisher, year, edition, pages
Society for Industrial & Applied Mathematics (SIAM), 2023
Keywords
 , graphical model, Bayesian network, causal discovery, characteristic imset, polytope, edge -walk, GES, max -min hill climbing
National Category
Discrete Mathematics
Identifiers
urn:nbn:se:kth:diva-326157 (URN)10.1137/21M1457205 (DOI)000955785600013 ()2-s2.0-85138137342 (Scopus ID)
Note
QC 20230425
2023-04-252023-04-252023-05-17Bibliographically approved