Causal Effect Identification with Context-specific Independence Relations of Control Variables
This work addresses a key limitation in causal inference for researchers by enabling more accurate effect estimation in scenarios with limited data, though it is incremental as it builds on existing NP-hard complexity results.
The authors tackled the problem of causal effect identification from observational data by incorporating context-specific independence (CSI) relations, proposing a sound and complete algorithm for a specific case and introducing graphical constraints that allow CSI relations to be learned from data, thereby expanding the set of identifiable causal effects beyond prior methods.
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.