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research-article

Comprehensible counterfactual explanation on Kolmogorov-Smirnov test

Authors:

Jian PeiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 14, Issue 9

Pages 1583 - 1596

https://doi.org/10.14778/3461535.3461546

Published: 01 May 2021 Publication History

Abstract

The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual explanations for test data failing the KS test. Concept-wise, we propose the notion of most comprehensible counterfactual explanations, which accommodates both the KS test data and the user domain knowledge in producing explanations. Computation-wise, we develop an efficient algorithm MOCHE (for <u>MO</u>st <u>C</u>ompre<u>H</u>ensible <u>E</u>xplanation) that avoids enumerating and checking an exponential number of subsets of the test set failing the KS test. MOCHE not only guarantees to produce the most comprehensible counterfactual explanations, but also is orders of magnitudes faster than the baselines. Experiment-wise, we present a systematic empirical study on a series of benchmark real datasets to verify the effectiveness, efficiency and scalability of most comprehensible counterfactual explanations and MOCHE.

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Cited By

Mondal MKhayati MSandlin HCudré-Mauroux P(2025)A survey of multimodal event detection based on data fusionThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00878-534:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00778-024-00878-5
Si MPei J(2024)Counterfactual Explanation of Shapley Value in Data CoalitionsProceedings of the VLDB Endowment10.14778/3681954.368200417:11(3332-3345)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682004
Verma SBoonsanong VHoang MHines KDickerson JShah C(2024)Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A ReviewACM Computing Surveys10.1145/367711956:12(1-42)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3677119

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 14, Issue 9

May 2021

249 pages

ISSN:2150-8097

Editors:
Xin Luna Dong
Amazon
,
Felix Naumann
HPI, University of Potsdam

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VLDB Endowment

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Published: 01 May 2021

Published in PVLDB Volume 14, Issue 9

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Cited By

Mondal MKhayati MSandlin HCudré-Mauroux P(2025)A survey of multimodal event detection based on data fusionThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00878-534:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00778-024-00878-5
Si MPei J(2024)Counterfactual Explanation of Shapley Value in Data CoalitionsProceedings of the VLDB Endowment10.14778/3681954.368200417:11(3332-3345)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682004
Verma SBoonsanong VHoang MHines KDickerson JShah C(2024)Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A ReviewACM Computing Surveys10.1145/367711956:12(1-42)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3677119

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