Abstract
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 respectively. In experiments on simulated Bayesian networks, we employ the PC algorithm
with different conditional independence tests for mixed
data, and show that the proposed approach outperforms
alternatives in terms of learning accuracy.
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