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

Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


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 provably 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.

Original languageEnglish
Pages (from-to)11237-11246
Number of pages10
JournalProceedings of Machine Learning Research
StatePublished - 2022
Externally publishedYes
Event25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022 - Virtual, Online, Spain
Duration: 28 Mar 202230 Mar 2022


Dive into the research topics of 'Causal Effect Identification with Context-specific Independence Relations of Control Variables'. Together they form a unique fingerprint.

Cite this