Polynomial Constraints in Causal Bayesian Networks

2012/06/20 by Changsung Kang, Kang, Changsung, Jin Tian +1
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.1206.5275

openalex publication_date 2012/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables. We show how we may reduce the complexity of the implicitization problem and make the problem tractable in certain causal Bayesian networks. We also show some preliminary results on the algebraic structure of polynomial constraints. The results have applications in distinguishing between causal models and in testing causal models with combined observational and experimental data.

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