Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. (arXiv:2211.02763v1 [cs.LG])

Bayesian causal structure learning aims to learn a posterior distribution
over directed acyclic graphs (DAGs), and the mechanisms that define the
relationship between parent and child variables. By taking a Bayesian approach,
it is possible to reason about the uncertainty of the causal model. The notion
of modelling the uncertainty over models is particularly crucial for causal
structure learning since the model could be unidentifiable when given only a
finite amount of observational data. In this paper, we introduce a …

arxiv bayes bayesian