局部与区域反事实规则：总结与稳健的补救措施
Local and Regional Counterfactual Rules: Summarized and Robust Recourses
AI总结：
提出基于随机森林的概率框架，生成稀疏局部与区域反事实规则，提供稳健且高概率的决策改变补救措施，并验证其有效性。
摘要：
反事实解释（CE）面临若干未解决的挑战，例如确保稳定性、综合多个CE以及提供合理性和稀疏性保证。从更实际的角度来看，最近的研究[Pawelczyk et al., 2022]表明，规定的反事实补救措施往往未被个体精确执行，并证明大多数最先进的CE算法在这种噪声环境中很可能失败。为了解决这些问题，我们提出了一个概率框架，为每个观测值提供稀疏的局部反事实规则，提供能够以高概率改变决策的值范围。这些规则作为多样化反事实解释的总结，并产生稳健的补救措施。我们进一步将这些局部规则聚合为区域反事实规则，识别数据子组的共享补救措施。我们的局部和区域规则源自随机森林算法，该算法通过在高密度区域选择补救措施来提供统计保证和对数据分布的保真度。此外，我们的规则是稀疏的，因为我们首先选择具有高概率改变决策的最小变量集。我们进行了实验以验证我们的反事实规则与标准CE和近期类似尝试相比的有效性。我们的方法可作为Python包使用。
英文摘要：
Counterfactual Explanations (CE) face several unresolved challenges, such as ensuring stability, synthesizing multiple CEs, and providing plausibility and sparsity guarantees. From a more practical point of view, recent studies [Pawelczyk et al., 2022] show that the prescribed counterfactual recourses are often not implemented exactly by individuals and demonstrate that most state-of-the-art CE algorithms are very likely to fail in this noisy environment. To address these issues, we propose a probabilistic framework that gives a sparse local counterfactual rule for each observation, providing rules that give a range of values capable of changing decisions with high probability. These rules serve as a summary of diverse counterfactual explanations and yield robust recourses. We further aggregate these local rules into a regional counterfactual rule, identifying shared recourses for subgroups of the data. Our local and regional rules are derived from the Random Forest algorithm, which offers statistical guarantees and fidelity to data distribution by selecting recourses in high-density regions. Moreover, our rules are sparse as we first select the smallest set of variables having a high probability of changing the decision. We have conducted experiments to validate the effectiveness of our counterfactual rules in comparison to standard CE and recent similar attempts. Our methods are available as a Python package.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
