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  Identifiability of causal graphs using functional models

Peters, J., Mooij, J., Janzing, D., & Schölkopf, B. (2011). Identifiability of causal graphs using functional models. In F. Cozman, & A. Pfeffer (Eds.), 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 589-598). Corvallis, OR, USA: AUAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB1E-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-0A97-9
Genre: Conference Paper

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http://www.auai.org/uai2011/ (Table of contents)
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 Creators:
Peters, J1, Author              
Mooij, J, Author              
Janzing, D1, Author              
Schölkopf, B1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independencebased causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm. In this work we propose an alternative by Identifiable Functional Model Classes (IFMOCs). As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph. To the best of our knowledge this is the first identifiability result of this kind that is not limited to linear functional relationships. We discuss how the IFMOC assumption and the Markov and faithfulness assumptions relate to each other and explain why we believe that the IFMOC assumption can be tested more easily on given data. We further provide a practical algorithm that recovers the causal graph from finitely many data; experiments on simulated data support the theoretical fndings.

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 Dates: 2011-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: PetersMJS2011
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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Place of Event: Barcelona, Spain
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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Source Genre: Proceedings
 Creator(s):
Cozman, FG, Editor
Pfeffer, A, Editor
Affiliations:
-
Publ. Info: Corvallis, OR, USA : AUAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 589 - 598 Identifier: ISBN: 978-0-9749039-7-2