A Characterization of Interventional Distributions in Semi-Markovian Causal Models

Jin Tian, Changsung Kang, Judea Pearl

We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network of unknown structure, in which some of the variables remain unmeasured. We show that such distributions are constrained by a simply formulated set of inequalities, from which bounds can be derived on causal effects that are not directly measured in randomized experiments.

Subjects: 9.1 Causality; 3.4 Probabilistic Reasoning


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