期刊
BERNOULLI
卷 25, 期 2, 页码 848-876出版社
INT STATISTICAL INST
DOI: 10.3150/17-BEJ1005
关键词
Bayesian network; DAG; nested Markov model; parameterization
资金
- National Institute on Aging
- EPSRC [EP/N020294/1]
- U.S. National Institutes of Health [R01 AI032475]
- U.S. Office of Naval Research [N00014-15-1-2672]
- SQuaRE grant from the American Institute of Mathematics
- EPSRC [EP/N020294/1] Funding Source: UKRI
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models are widely used in causal inference and machine learning. We explicitly evaluate their dimension, show that they are curved exponential families of distributions, and fit them to data. The parameterization avoids the irregularities and unidentifiability of latent variable models. The parameters used are all fully identifiable and causally-interpretable quantities.
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