Bayesian Network Learning with Discrete Case-Control Data

Giorgos Borboudakis, Ioannis Tsamardinos
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:556-565, 2015.

Abstract

We address the problem of learning Bayesian networks from discrete, unmatched case- control data using specialized conditional in- dependence tests. Those tests can also be used for learning other types of graphical models or for feature selection. We also propose a post-processing method that can be applied in conjunction with any Bayesian network learning algorithm. In simulations we show that our methods are able to deal with selection bias from case-control data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-borboudakis15a, title = {{B}ayesian Network Learning with Discrete Case-Control Data}, author = {Borboudakis, Giorgos and Tsamardinos, Ioannis}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {556--565}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/borboudakis15a/borboudakis15a.pdf}, url = {https://proceedings.mlr.press/r13/borboudakis15a.html}, abstract = {We address the problem of learning Bayesian networks from discrete, unmatched case- control data using specialized conditional in- dependence tests. Those tests can also be used for learning other types of graphical models or for feature selection. We also propose a post-processing method that can be applied in conjunction with any Bayesian network learning algorithm. In simulations we show that our methods are able to deal with selection bias from case-control data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Bayesian Network Learning with Discrete Case-Control Data %A Giorgos Borboudakis %A Ioannis Tsamardinos %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-borboudakis15a %I PMLR %P 556--565 %U https://proceedings.mlr.press/r13/borboudakis15a.html %V R13 %X We address the problem of learning Bayesian networks from discrete, unmatched case- control data using specialized conditional in- dependence tests. Those tests can also be used for learning other types of graphical models or for feature selection. We also propose a post-processing method that can be applied in conjunction with any Bayesian network learning algorithm. In simulations we show that our methods are able to deal with selection bias from case-control data. %Z Reissued by PMLR on 04 October 2026.
APA
Borboudakis, G. & Tsamardinos, I.. (2015). Bayesian Network Learning with Discrete Case-Control Data. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:556-565 Available from https://proceedings.mlr.press/r13/borboudakis15a.html. Reissued by PMLR on 04 October 2026.

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