Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints

Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can… CONTINUE READING

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