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Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
Jeong, Hyunchai Ejaz, Adiba Tian, Jin Bareinboim, Elias
Jeong, Hyunchai
Ejaz, Adiba
Tian, Jin
Bareinboim, Elias
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Testing a hypothesized causal model against observational data is a key prerequisite for many causal inference tasks. A natural approach is to test whether the conditional independence relations (CIs) assumed in the model hold in the data. While a model can assume exponentially many CIs (with respect to the number of variables), testing all of them is both impractical and unnecessary. Causal graphs, which encode these CIs in polynomial space, give rise to local Markov properties that enable model testing with a significantly smaller subset of CIs. Model testing based on local properties requires an algorithm to list the relevant CIs. However, existing algorithms for realistic settings with hidden variables and non-parametric distributions can take exponential time to produce even a single CI constraint. In this paper, we introduce the c-component local Markov property (C-LMP) for causal graphs with hidden variables. Since C-LMP can still invoke an exponential number of CIs, we develop a polynomial delay algorithm to list these CIs in poly-time intervals. To our knowledge, this is the first algorithm that enables poly-delay testing of CIs in causal graphs with hidden variables against arbitrary data distributions. Experiments on real-world and synthetic data demonstrate the practicality of our algorithm.
Citation
H. Jeong, A. Ejaz, J. Tian, and E. Bareinboim, “Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 25, pp. 26813–26822, Apr. 2025, doi: 10.1609/AAAI.V39I25.34885.
Source
Proceedings of the AAAI Conference on Artificial Intelligence
Conference
39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Keywords
Causal graph, Causal inferences, Causal modeling, Conditional independences, Hidden variable, Markov property, Model testing
Subjects
Source
39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Publisher
Association for the Advancement of Artificial Intelligence