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Learning Causal Models Of Relational Domains

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 talk, I give a brief overview of causality. I describe the traditional approach to discovering causal knowledge from data. And from there, I explain why those approaches aren't sufficient and describe recent research efforts that extend causal discovery to relational domains.