Learning Causal Models of Relational Domains

By Marc E. Maier, Brian J. Taylor, Hüseyin Oktay, David D. Jensen. 2010

48 citations recorded by the source corpus.

Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge representations for propositional domains. In this paper, we present several key algorithmic and theoretical innovations that extend causal discovery to relational domains. We provide strong evidence that effective learning of causal models is enhanced by relational representations. We present an algorithm, relational PC, that learns causal dependencies in a state-of-the-art relational representation, and we identify the key representational and algorithmic innovations that make the algorithm possible. Finally, we prove the algorithm's theoretical correctness and demonstrate its effectiveness on synthetic and real data sets.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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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…”
Causal discovery in social media using quasi-experimental designs (2010, background): “Maier and coauthors [16] extended the PC algorithm to relational domains, pushing causal discovery into 0 10 20 30 40 50 60 -2 -1 0 1 2”
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms (2014, background): “Maier et al.(39) state, ‘‘the directed acyclic graph (DAG) has been shown to be sufficient to represent causal knowledge.”
A Sound and Complete Algorithm for Learning Causal Models from Relational Data (2013, methodology): “The single algorithm that does address causality—Relational PC (Maier et al., 2010)—is not complete and is prone to orientation errors, as we show in this paper.”