Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions. While Judea Pearl's influential approach is theoretically elegant, its generation of a counterfactual scenario often requires interventions that are too detached from the real scenarios to be feasible. In response, we propose a framework of natural counterfactuals and a method for generating counterfactuals that are natural with respect to the actual world's data distribution. Our methodology refines counterfactual reasoning, allowing changes in causally preceding variables to minimize deviations from realistic scenarios. To generate natural counterfactuals, we introduce an innovative optimization framework that permits but controls the extent of backtracking with a naturalness criterion. Empirical experiments indicate the effectiveness of our method.

翻译：反事实推理在人类认知中至关重要，尤其在提供解释和做出决策时发挥关键作用。尽管朱迪亚·珀尔提出的影响深远的方法在理论上优雅，但其对反事实场景的生成往往需要干预，这些干预与现实场景过于脱节而难以实现。为此，我们提出了一种自然反事实框架及相应生成方法，能够生成符合现实世界数据分布的反事实情形。该方法优化了反事实推理过程，允许对因果前因变量进行调整，以最小化与真实场景的偏差。为生成自然反事实，我们引入了一种创新性优化框架，通过自然性准则允许但控制回溯程度。实验结果表明了该方法的有效性。