A Sound and Complete Algorithm for Learning Causal Models from Relational Data

Abstract

The PC algorithm learns maximally oriented causal Bayesian networks. However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks. Recent developments in the theory and representation of relational models support lifted reasoning about conditional independence. This enables a powerful constraint for orienting bivariate dependencies and forms the basis of a new algorithm for learning structure. We present the relational causal discovery (RCD) algorithm that learns causal relational models. We prove that RCD is sound and complete, and we present empirical results that demonstrate effectiveness.

Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)

References in corpus (3)

• Causal Inference and Causal Explanation with Background Knowledge
• A Bayesian Approach to Constraint Based Causal Inference
• Reasoning about Independence in Probabilistic Models of Relational Data

Cited by in corpus (5)

• Artificial Intelligence for Social Good
• Reasoning about Independence in Probabilistic Models of Relational Data
• Causal Inference Under Interference And Network Uncertainty
• Probabilistic Relational Model Benchmark Generation
• Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning