MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data

Author:

Redelmeier Annabelle,Jullum MartinORCID,Aas Kjersti,Løland Anders

Funder

Norges Forskningsråd

HORIZON EUROPE Digital, Industry and Space

Publisher

Springer Science and Business Media LLC

Reference42 articles.

1. Antorán J, Bhatt U, Adel T et al (2021) Getting a clue: a method for explaining uncertainty estimates. In: International Conference on Learning Representations

2. Borisov V, Seffler K, Leemann T et al (2023) Language models are realistic tabular data generators. In: Proceedings of ICLR 2023

3. Breiman L, Friedman J, Olshen R et al (1984) Classification and regression trees. Chapman and Hall, Boca Raton

4. Brughmans D, Leyman P, Martens D (2023) NICE: an algorithm for nearest instance counterfactual explanations. Data Mining Knowl Discov pp 1–39

5. Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining, pp 785–794

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