Causal Identification under Markov Equivalence


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Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph. In many practical settings, however, the knowledge available for the researcher is not strong enough so as to specify a unique causal graph. Another line of investigation attempts to use observational data to learn a qualitative description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of observed features. In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, represented by a partial ancestral graph (PAG). We start by deriving a set of graphical properties of PAGs that are carried over to its induced subgraphs. We then develop an algorithm to compute the effect of an arbitrary set of variables on an arbitrary outcome set. We show that the algorithm is strictly more powerful than the current state of the art found in the literature.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Fourth Conference (2018) : Uncertainty in Artificial Intelligence
PublisherAssociation for Uncertainty in Artificial Intelligence (AUAI)
ISBN (Print)9780996643139
Publication statusPublished - Aug 2018
Event34th Conference on Uncertainty in Artificial Intelligence - Monterey, United States
Duration: 6 Aug 201810 Aug 2018


Conference34th Conference on Uncertainty in Artificial Intelligence
Abbreviated titleUAI2018
Country/TerritoryUnited States
Internet address

Bibliographical note

We thank Sanghack Lee and the reviewers for all the feedback provided. Bareinboim and Jaber are supported in parts by grants from NSF IIS-1704352 and IIS1750807 (CAREER). Zhang is supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU13600715.


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