@inproceedings{6ce494b645a74731b41616693b9062c4,
title = "Investigating the effect of binning on causal discovery",
abstract = "Binning (a.k.a. discretization) of numerically continuous measurements is a wide-spread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.",
keywords = "Causal Discovery, Data discretization, Greedy Equivalence Search (GES), Search Performance",
author = "Deckert, {Andrew Colt} and Erich Kummerfeld",
year = "2019",
month = nov,
doi = "10.1109/BIBM47256.2019.8983336",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2574--2581",
editor = "Illhoi Yoo and Jinbo Bi and Hu, {Xiaohua Tony}",
booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",
note = "2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",
}