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Constraint-based causal discovery with mixed data
2018
International Journal of Data Science and Analytics
We address the problem of constraint-based causal discovery with mixed data types, such as (but not limited to) continuous, binary, multinomial, and ordinal variables. We use likelihood-ratio tests based on appropriate regression models and show how to derive symmetric conditional independence tests. Such tests can then be directly used by existing constraint-based methods with mixed data, such as the PC and FCI algorithms for learning Bayesian networks and maximal ancestral graphs,
doi:10.1007/s41060-018-0097-y
pmid:30957008
pmcid:PMC6428307
dblp:journals/ijdsa/TsagrisBLT18
fatcat:2mn5wm77fzhavckbixzc7weyfq