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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 24 / No. 1: Twenty-Fourth AAAI Conference on Artificial Intelligence

Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features

March 8, 2023

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Authors

Eun Yong Kang

University of California, Los Angeles


Ilya Shpitser

Harvard School of Public Health


Eleazar Eskin

University of California, Los Angeles


DOI:

10.1609/aaai.v24i1.7756


Abstract:

There have been many efforts to identify causal graphical features such as directed edges between random variables from observational data. Recently, Tian et al. proposed a new dynamic programming algorithm which computes marginalized posterior probabilities of directed edge features over all the possible structures in O(n3n) time when the number of parents per node is bounded by a constant, where n is the number of variables of interest. However the main drawback of this approach is that deciding a single appropriate threshold for the existence of the directed edge feature is difficult due to the scale difference of the posterior probabilities between the directed edges forming v-structures and the directed edges not forming v-structures. We claim that computing posterior probabilities of both adjacencies and v-structures is necessary and more effective for discovering causal graphical features, since it allows us to find a single appropriate decision threshold for the existence of the feature that we are testing. For efficient computation, we provide a novel dynamic programming algorithm which computes the posterior probabilities of all of n(n – 1)/2 adjacency and n(n–1 choose 2) v-structure features in O(n3 * 3n) time.

Topics: AAAI

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HOW TO CITE:

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features Proceedings of the AAAI Conference on Artificial Intelligence, 24 (2010) 1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features AAAI 2010, 1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin (2010). Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features. Proceedings of the AAAI Conference on Artificial Intelligence, 24, 1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin. Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features. Proceedings of the AAAI Conference on Artificial Intelligence, 24 2010 p.1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin. 2010. Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features. "Proceedings of the AAAI Conference on Artificial Intelligence, 24". 1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin. (2010) "Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features", Proceedings of the AAAI Conference on Artificial Intelligence, 24, p.1175

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin, "Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features", AAAI, p.1175, 2010.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin. "Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features". Proceedings of the AAAI Conference on Artificial Intelligence, 24, 2010, p.1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin. "Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features". Proceedings of the AAAI Conference on Artificial Intelligence, 24, (2010): 1175.

Eun Yong Kang|| Ilya Shpitser|| Eleazar Eskin. Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features. AAAI[Internet]. 2010[cited 2023]; 1175.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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