Algorithmic syntactic causal identification

arXiv:2403.09580v1 Announce Type: cross
Abstract: Causal identification in causal Bayes nets (CBNs) is an important tool in causal inference allowing the derivation of interventional distributions from observational distributions where this is possible in principle. However, most existing formulations of causal identification using techniques such as d-separation and do-calculus are expressed within the mathematical language of classical probability theory on CBNs. However, there are many causal settings where probability theory and hence current causal identification techniques are inapplicable such as relational …

abstract arxiv bayes calculus causal causal inference cs.ai cs.lg derivation however identification inference language stat.ot tool type