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International Journal of Data Science and Analytics
Erschienen in: International Journal of Data Science and Analytics 1/2018

Open Access 02.02.2018 | Regular Paper

Constraint-based causal discovery with mixed data

verfasst von: Michail Tsagris, Giorgos Borboudakis, Vincenzo Lagani, Ioannis Tsamardinos

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 1/2018

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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.
Vorheriger Artikel Scoring Bayesian networks of mixed variables
Nächster Artikel Correction to: Constraint-based causal discovery with mixed data

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Fußnoten
1
In this case, the t test (Wald test) is equivalent to the F test (likelihood-ratio test).
 
2
The reason of this is that, these measure being proportions, the absolute difference does not reflect the same information as the ratio which is more meaningful. The difference, for example, between 0.2 and 0.1 is the same as that of 0.8 and 0.7, but their ratio is clearly not the same.
 
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Metadaten
Titel
Constraint-based causal discovery with mixed data
verfasst von
Michail Tsagris
Giorgos Borboudakis
Vincenzo Lagani
Ioannis Tsamardinos
Publikationsdatum
02.02.2018
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 1/2018
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-018-0097-y

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