A Sound and Complete Algorithm for Learning Causal Models from Relational Data
This provides a foundational solution for researchers and practitioners in AI and statistics dealing with relational data, representing a novel method rather than an incremental improvement.
The paper tackles the problem of learning causal models from relational data, for which no complete algorithm existed, and presents the relational causal discovery (RCD) algorithm that is proven sound and complete, with empirical results showing effectiveness.
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.