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 too much deviation from the observed scenarios to be feasible, as we show using simple examples. To mitigate this difficulty, we propose a framework of \emph{natural counterfactuals} and a method for generating counterfactuals that are more feasible with respect to the actual data distribution. Our methodology incorporates a certain amount of backtracking when needed, allowing changes in causally preceding variables to minimize deviations from realistic scenarios. Specifically, we introduce a novel optimization framework that permits but also controls the extent of backtracking with a naturalness criterion. Empirical experiments demonstrate the effectiveness of our method. The code is available at https://github.com/GuangyuanHao/natural_counterfactuals.

翻译：反事实推理在人类认知中具有关键作用，尤其在提供解释和做出决策时尤为重要。尽管Judea Pearl提出的影响深远的方法在理论上十分优雅，但我们通过简单示例表明，其生成的反事实情景往往需要过多偏离已观测情景，导致可行性不足。为缓解这一困难，我们提出了一个“自然反事实”框架，以及一种生成更符合实际数据分布的反事实的方法。我们的方法在必要时引入一定程度的回溯，允许改变因果先导变量，以最小化对现实情景的偏离。具体而言，我们引入了一种新颖的优化框架，该框架允许回溯但通过自然性准则控制回溯的程度。实证实验证明了我们方法的有效性。代码可在 https://github.com/GuangyuanHao/natural_counterfactuals 获取。