CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests

Autor:	Dandl, Susanne, Blesch, Kristin, Freiesleben, Timo, König, Gunnar, Kapar, Jan, Bischl, Bernd, Wright, Marvin
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavior, they hint users towards possible actions and give grounds for contesting decisions. As a crucial factor in achieving these goals, counterfactuals must be plausible, i.e., describing realistic alternative scenarios within the data manifold. This paper leverages a recently developed generative modeling technique -- adversarial random forests (ARFs) -- to efficiently generate plausible counterfactuals in a model-agnostic way. ARFs can serve as a plausibility measure or directly generate counterfactual explanations. Our ARF-based approach surpasses the limitations of existing methods that aim to generate plausible counterfactual explanations: It is easy to train and computationally highly efficient, handles continuous and categorical data naturally, and allows integrating additional desiderata such as sparsity in a straightforward manner. Comment: SD, KB, TB, and GK contributed equally as first authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.03506 Zobrazit plný text záznamu View this record from Arxiv