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We provide a parameterization of the discrete nested Markov model, which is asupermodel that approximates DAG models (Bayesian network models) with latentvariables. Such models are widely used in causal inference and machinelearning. We explicitly evaluate their dimension, show that they are curvedexponential families of distributions, and fit them to data. Theparameterization avoids the irregularities and unidentifiability of latentvariable models. The parameters used are all fully identifiable andcausally-interpretable quantities.

Smooth, identifiable supermodels of discrete DAG models with latent variables