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Bayesian network structure learning with causal effects in the presence of latent variables
[article]
2020
arXiv
pre-print
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships. This paper describes a hybrid structure learning algorithm, called CCHM, which
arXiv:2005.14381v2
fatcat:ttag3mx5krar7lu2tam5umi6sq