Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints
By Fattaneh Jabbari, J. Ramsey, Peter Spirtes, Gregory F. Cooper. 2017
36 citations recorded by the source corpus.
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 generate and probabilistically score multiple models, outputting the most probable one; however, they are often computationally infeasible to apply when modeling latent variables. We introduce a hybrid method that derives a Bayesian probability that the set of independence tests associated with a given causal model are jointly correct. Using this constraint-based scoring method, we are able to score multiple causal models, which possibly contain latent variables, and output the most probable one. The structure-discovery performance of the proposed method is compared to an existing constraint-based method (RFCI) using data generated from several previously published Bayesian networks. The structural Hamming distances of the output models improved when using the proposed method compared to RFCI, especially for small sample sizes.
Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research
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Citations in the corpus
- Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “In this context, several methods have been proposed to perform causal graphical model structure learning with latent variables (Leray et al., 2008; Jabbari et al., 2017; Raghu et al., 2018).”