Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood
Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training data. We introduce MissCNF, which trains CNFs directly on incomplete data by maximizing the marginal likelihood of each partially observed sample, without discarding rows o...
Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann et al.
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