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方面具有特殊意义。尽管朱迪亚·珀尔提出的经典方法在理论上具有优雅性，但其生成反事实场景所需的干预往往与现实场景脱节，导致可行性不足。为此，我们提出了一种自然反事实框架及相应生成方法，使得生成的反对事实能贴合实际世界的数据分布。该方法优化了反事实推理过程，允许改变因果前置变量，从而最小化与真实场景的偏差。为实现自然反事实的生成，我们引入了一个创新优化框架，该框架以自然性准则为约束，允许但控制回溯程度。实验结果表明了该方法的有效性。