Explainable Data-Driven Optimization: From Context to Decision and Back Again

Alexandre Forel, Axel Parmentier, Thibaut Vidal
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:10170-10187, 2023.

Abstract

Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms remains unaddressed. This lack of interpretability can block the adoption of data-driven solutions as practitioners may not understand or trust the recommended decisions. We bridge this gap by introducing a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We introduce two classes of explanations and develop methods to find nearest explanations of random forest and nearest-neighbor predictors. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-forel23a, title = {Explainable Data-Driven Optimization: From Context to Decision and Back Again}, author = {Forel, Alexandre and Parmentier, Axel and Vidal, Thibaut}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {10170--10187}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/forel23a/forel23a.pdf}, url = {https://proceedings.mlr.press/v202/forel23a.html}, abstract = {Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms remains unaddressed. This lack of interpretability can block the adoption of data-driven solutions as practitioners may not understand or trust the recommended decisions. We bridge this gap by introducing a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We introduce two classes of explanations and develop methods to find nearest explanations of random forest and nearest-neighbor predictors. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.} }
Endnote
%0 Conference Paper %T Explainable Data-Driven Optimization: From Context to Decision and Back Again %A Alexandre Forel %A Axel Parmentier %A Thibaut Vidal %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-forel23a %I PMLR %P 10170--10187 %U https://proceedings.mlr.press/v202/forel23a.html %V 202 %X Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms remains unaddressed. This lack of interpretability can block the adoption of data-driven solutions as practitioners may not understand or trust the recommended decisions. We bridge this gap by introducing a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We introduce two classes of explanations and develop methods to find nearest explanations of random forest and nearest-neighbor predictors. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
APA
Forel, A., Parmentier, A. & Vidal, T.. (2023). Explainable Data-Driven Optimization: From Context to Decision and Back Again. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:10170-10187 Available from https://proceedings.mlr.press/v202/forel23a.html.

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