A Characterization of Interventional Distributions in Semi-Markovian Causal Models

Proceedings of the AAAI Conference on Artificial Intelligence, 21

Volume

Technical Papers

Uncertainty in AI

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.