Causal Effect Identification with Context-specific Independence Relations of Control Variables

Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:11237-11246, 2022.

Abstract

We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the causal effects is proposed in Tikka et al. (2019), no complete algorithm for the task exists. In this work, we propose a sound and complete algorithm for the setting when the CSI relations are limited to observed nodes with no parents in the causal graph. One limitation of the state of the art in terms of its applicability is that the CSI relations among all variables, even unobserved ones, must be given (as opposed to learned). Instead, We introduce a set of graphical constraints under which the CSI relations can be learned from mere observational distribution. This expands the set of identifiable causal effects beyond the state of the art.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-mokhtarian22a, title = { Causal Effect Identification with Context-specific Independence Relations of Control Variables }, author = {Mokhtarian, Ehsan and Jamshidi, Fateme and Etesami, Jalal and Kiyavash, Negar}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {11237--11246}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/mokhtarian22a/mokhtarian22a.pdf}, url = {https://proceedings.mlr.press/v151/mokhtarian22a.html}, abstract = { We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the causal effects is proposed in Tikka et al. (2019), no complete algorithm for the task exists. In this work, we propose a sound and complete algorithm for the setting when the CSI relations are limited to observed nodes with no parents in the causal graph. One limitation of the state of the art in terms of its applicability is that the CSI relations among all variables, even unobserved ones, must be given (as opposed to learned). Instead, We introduce a set of graphical constraints under which the CSI relations can be learned from mere observational distribution. This expands the set of identifiable causal effects beyond the state of the art. } }
Endnote
%0 Conference Paper %T Causal Effect Identification with Context-specific Independence Relations of Control Variables %A Ehsan Mokhtarian %A Fateme Jamshidi %A Jalal Etesami %A Negar Kiyavash %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-mokhtarian22a %I PMLR %P 11237--11246 %U https://proceedings.mlr.press/v151/mokhtarian22a.html %V 151 %X We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the causal effects is proposed in Tikka et al. (2019), no complete algorithm for the task exists. In this work, we propose a sound and complete algorithm for the setting when the CSI relations are limited to observed nodes with no parents in the causal graph. One limitation of the state of the art in terms of its applicability is that the CSI relations among all variables, even unobserved ones, must be given (as opposed to learned). Instead, We introduce a set of graphical constraints under which the CSI relations can be learned from mere observational distribution. This expands the set of identifiable causal effects beyond the state of the art.
APA
Mokhtarian, E., Jamshidi, F., Etesami, J. & Kiyavash, N.. (2022). Causal Effect Identification with Context-specific Independence Relations of Control Variables . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:11237-11246 Available from https://proceedings.mlr.press/v151/mokhtarian22a.html.

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