Counterfactual reasoning -- envisioning hypothetical scenarios, or possible worlds, where some circumstances are different from what (f)actually occurred (counter-to-fact) -- is ubiquitous in human cognition. Conventionally, counterfactually-altered circumstances have been treated as "small miracles" that locally violate the laws of nature while sharing the same initial conditions. In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists. Instead, they suggest a backtracking account of counterfactuals, according to which the causal laws remain unchanged in the counterfactual world; differences to the factual world are instead "backtracked" to altered initial conditions (exogenous variables). In the present work, we explore and formalise this alternative mode of counterfactual reasoning within the SCM framework. Despite ample evidence that humans backtrack, the present work constitutes, to the best of our knowledge, the first general account and algorithmisation of backtracking counterfactuals. We discuss our backtracking semantics in the context of related literature and draw connections to recent developments in explainable artificial intelligence (XAI).

翻译：反事实推理——设想假设情景或可能世界，其中某些情况与实际发生的事实不同（反事实）——在人类认知中无处不在。传统上，反事实改变的情景被视为“小奇迹”，它们局部违反自然规律，但共享相同的初始条件。在珀尔的结构因果模型框架中，这通过干预措施在数学上得到严格定义，干预修改了因果规律，而外生变量的值保持不变。然而，近年来，这种纯粹干预主义的反事实解释日益受到哲学家和心理学家的审视。相反，他们提出了反事实的回溯解释，根据这一解释，因果规律在反事实世界中保持不变；与事实世界的差异反而被“回溯”到改变的初始条件（外生变量）。在本文中，我们探索并在SCM框架内形式化了这种反事实推理的替代模式。尽管有充分证据表明人类会回溯，但据我们所知，本文首次提出了回溯反事实的通用解释和算法化。我们在相关文献的背景下讨论回溯语义学，并将其与可解释人工智能的最新发展联系起来。