论文标题
本地和区域反事实规则：总结和稳健的回复
Local and Regional Counterfactual Rules: Summarized and Robust Recourses
论文作者
论文摘要
反事实解释（CE）面临着几个尚未解决的挑战，例如确保稳定性，综合多个CES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等，2022]表明，规定的反事实回复通常不是由个人确切实现的，并证明大多数最新的CE算法在这个嘈杂的环境中很可能会失败。为了解决这些问题，我们提出了一个概率框架，该框架为每个观察结果提供了稀疏的本地反事实规则，提供了一系列价值的规则，这些价值范围具有很高的可能性。这些规则是各种反事实解释的摘要，并产生了强大的回报。我们将这些本地规则进一步汇总为区域反事实规则，确定数据子组的共享回流。我们的本地和区域规则源自随机森林算法，该算法通过选择高密度区域的回流来提供统计保证和忠于数据分布的保真度。此外，我们的规则很稀疏，因为我们首先选择具有更改决策可能性的最小变量集。与标准CE和最近的类似尝试相比，我们进行了实验，以验证反事实规则的有效性。我们的方法可作为Python软件包使用。
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.
