IDA with Background Knowledge

Zhuangyan Fang, Yangbo He
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
more » ... direct causal information. Furthermore, we consider non-ancestral information and prove that it can be equivalently transformed into direct causal information, meaning that we can also locally estimate all possible causal effects with non-ancestral information. The test results on both synthetic and real-world data sets show that our methods are efficient and stable.
dblp:conf/uai/FangH20 fatcat:hwttgwdp2zclzny3hgawtqsjbe