Causal structure-based root cause analysis of outliers

Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2357-2369, 2022.

Abstract

Current techniques for explaining outliers cannot tell what caused the outliers. We present a formal method to identify "root causes" of outliers, amongst variables. The method requires a causal graph of the variables along with the functional causal model. It quantifies the contribution of each variable to the target outlier score, which explains to what extent each variable is a "root cause" of the target outlier. We study the empirical performance of the method through simulations and present a real-world case study identifying "root causes" of extreme river flows.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-budhathoki22a, title = {Causal structure-based root cause analysis of outliers}, author = {Budhathoki, Kailash and Minorics, Lenon and Bloebaum, Patrick and Janzing, Dominik}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {2357--2369}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/budhathoki22a/budhathoki22a.pdf}, url = {https://proceedings.mlr.press/v162/budhathoki22a.html}, abstract = {Current techniques for explaining outliers cannot tell what caused the outliers. We present a formal method to identify "root causes" of outliers, amongst variables. The method requires a causal graph of the variables along with the functional causal model. It quantifies the contribution of each variable to the target outlier score, which explains to what extent each variable is a "root cause" of the target outlier. We study the empirical performance of the method through simulations and present a real-world case study identifying "root causes" of extreme river flows.} }
Endnote
%0 Conference Paper %T Causal structure-based root cause analysis of outliers %A Kailash Budhathoki %A Lenon Minorics %A Patrick Bloebaum %A Dominik Janzing %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-budhathoki22a %I PMLR %P 2357--2369 %U https://proceedings.mlr.press/v162/budhathoki22a.html %V 162 %X Current techniques for explaining outliers cannot tell what caused the outliers. We present a formal method to identify "root causes" of outliers, amongst variables. The method requires a causal graph of the variables along with the functional causal model. It quantifies the contribution of each variable to the target outlier score, which explains to what extent each variable is a "root cause" of the target outlier. We study the empirical performance of the method through simulations and present a real-world case study identifying "root causes" of extreme river flows.
APA
Budhathoki, K., Minorics, L., Bloebaum, P. & Janzing, D.. (2022). Causal structure-based root cause analysis of outliers. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:2357-2369 Available from https://proceedings.mlr.press/v162/budhathoki22a.html.

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