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IDA with Background Knowledge
2020
Conference on Uncertainty in Artificial Intelligence
In this paper, we consider the problem of estimating all possible causal effects from observational data with two types of background knowledge: direct causal information and nonancestral information. Following the IDA framework, we first provide locally valid orientation rules for maximal partially directed acyclic graphs (PDAGs), which are widely used to represent background knowledge. Based on the proposed rules, we present a fully local algorithm to estimate all possible causal effects with
dblp:conf/uai/FangH20
fatcat:hwttgwdp2zclzny3hgawtqsjbe