Investigating the effect of binning on causal discovery

Andrew Colt Deckert; Erich Kummerfeld

doi:10.1109/BIBM47256.2019.8983336

Investigating the effect of binning on causal discovery

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2574-2581
Number of pages	8
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983336
State	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	11/18/19 → 11/21/19

Bibliographical note

Funding Information:
This work was supported by funding from NCRR 1UL1TR002494-01 to EK.

Publisher Copyright:
© 2019 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Causal Discovery
Data discretization
Greedy Equivalence Search (GES)
Search Performance

Publisher link

10.1109/BIBM47256.2019.8983336

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Deckert, A. C., & Kummerfeld, E. (2019). Investigating the effect of binning on causal discovery. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 2574-2581). Article 8983336 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983336

Investigating the effect of binning on causal discovery. / Deckert, Andrew Colt ; Kummerfeld, Erich.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2574-2581 8983336 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Deckert, AC & Kummerfeld, E 2019, Investigating the effect of binning on causal discovery. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983336, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 2574-2581, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 11/18/19. https://doi.org/10.1109/BIBM47256.2019.8983336

Deckert AC , Kummerfeld E. Investigating the effect of binning on causal discovery. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2574-2581. 8983336. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983336

Deckert, Andrew Colt ; Kummerfeld, Erich. / Investigating the effect of binning on causal discovery. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2574-2581 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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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",

note = "Funding Information: This work was supported by funding from NCRR 1UL1TR002494-01 to EK. Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

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Investigating the effect of binning on causal discovery

Abstract

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Keywords

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