Abstract:Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or "associational") languages. Despite several senses in which causal reasoning is indeed more complex-both expressively and inferentially-we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to p… Show more
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