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

By Marc E. Maier, Katerina Marazopoulou, David T. Arbour, David D. Jensen. 2013

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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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, methodology): “…more conceptual and generative models, techniques such as Bayesian Program Learning (Lake et al., 2015), learning models of causal relationships (Maier et al., 2010, 2013; Aalen et al., 2016), and stochastic grammars to learn more inter-pretable structures (Brendel and Todorovic, 2011; Zhou and…”