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Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect. (arXiv:2208.04627v2 [cs.LG] UPDATED)
Aug. 16, 2022, 1:11 a.m. | Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew J. Vowels, Jalal Etesami, Negar Kiyavash
cs.LG updates on arXiv.org arxiv.org
Causal identification is at the core of the causal inference literature,
where complete algorithms have been proposed to identify causal queries of
interest. The validity of these algorithms hinges on the restrictive assumption
of having access to a correctly specified causal structure. In this work, we
study the setting where a probabilistic model of the causal structure is
available. Specifically, the edges in a causal graph are assigned probabilities
which may, for example, represent degree of belief from domain experts. …
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