CountARFactuals -- Generating plausible model-agnostic counterfactual
  explanations with adversarial random forests
Susanne Dandl Kristin Blesch Timo Freiesleben Gunnar König Jan Kapar Bernd Bischl Marvin Wright
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.