Causal Identification under Markov Equivalence

Amin Jaber, Jiji Zhang, Elias Bareinboim
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:977-986, 2018.

Abstract

Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The prob- lem of identification of causal effects is con- cerned with determining whether a causal ef- fect 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 qualita- tive description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of ob- served features. In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, repre- sented by a partial ancestral graph (PAG). We start by deriving a set of graphical properties of PAGs that are carried over to its induced sub- graphs. We then develop an algorithm to com- pute 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-jaber18a, title = {Causal Identification under {M}arkov Equivalence}, author = {Jaber, Amin and Zhang, Jiji and Bareinboim, Elias}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {977--986}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/jaber18a/jaber18a.pdf}, url = {https://proceedings.mlr.press/r16/jaber18a.html}, abstract = {Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The prob- lem of identification of causal effects is con- cerned with determining whether a causal ef- fect 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 qualita- tive description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of ob- served features. In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, repre- sented by a partial ancestral graph (PAG). We start by deriving a set of graphical properties of PAGs that are carried over to its induced sub- graphs. We then develop an algorithm to com- pute 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Causal Identification under Markov Equivalence %A Amin Jaber %A Jiji Zhang %A Elias Bareinboim %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-jaber18a %I PMLR %P 977--986 %U https://proceedings.mlr.press/r16/jaber18a.html %V R16 %X Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The prob- lem of identification of causal effects is con- cerned with determining whether a causal ef- fect 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 qualita- tive description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of ob- served features. In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, repre- sented by a partial ancestral graph (PAG). We start by deriving a set of graphical properties of PAGs that are carried over to its induced sub- graphs. We then develop an algorithm to com- pute 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. %Z Reissued by PMLR on 04 October 2026.
APA
Jaber, A., Zhang, J. & Bareinboim, E.. (2018). Causal Identification under Markov Equivalence. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:977-986 Available from https://proceedings.mlr.press/r16/jaber18a.html. Reissued by PMLR on 04 October 2026.

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