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