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Counterfactual Explanations (CE) face several unresolved challenges, such asensuring stability, synthesizing multiple CEs, and providing plausibility andsparsity guarantees. From a more practical point of view, recent studies[Pawelczyk et al., 2022] show that the prescribed counterfactual recourses areoften not implemented exactly by individuals and demonstrate that moststate-of-the-art CE algorithms are very likely to fail in this noisyenvironment. To address these issues, we propose a probabilistic framework thatgives a sparse local counterfactual rule for each observation, providing rulesthat give a range of values capable of changing decisions with highprobability. These rules serve as a summary of diverse counterfactualexplanations and yield robust recourses. We further aggregate these local rulesinto a regional counterfactual rule, identifying shared recourses for subgroupsof the data. Our local and regional rules are derived from the Random Forestalgorithm, which offers statistical guarantees and fidelity to datadistribution by selecting recourses in high-density regions. Moreover, ourrules are sparse as we first select the smallest set of variables having a highprobability of changing the decision. We have conducted experiments to validatethe effectiveness of our counterfactual rules in comparison to standard CE andrecent similar attempts. Our methods are available as a Python package.

Rethinking Counterfactual Explanations as Local and Regional Counterfactual Policies