In the realm of causal inference, Potential Outcomes (PO) and Structural Causal Models (SCM) are recognized as the principal frameworks.However, when it comes to Layer 3 valuations -- counterfactual queries deeply entwined with individual-level semantics -- both frameworks encounter limitations due to the degenerative issues brought forth by the consistency rule. This paper advocates for the Distribution-consistency Structural Causal Models (DiscoSCM) framework as a pioneering approach to counterfactual inference, skillfully integrating the strengths of both PO and SCM. The DiscoSCM framework distinctively incorporates a unit selection variable $U$ and embraces the concept of uncontrollable exogenous noise realization. Through personalized incentive scenarios, we demonstrate the inadequacies of PO and SCM frameworks in representing the probability of a user being a complier (a Layer 3 event) without degeneration, an issue adeptly resolved by adopting the assumption of independent counterfactual noises within DiscoSCM. This innovative assumption broadens the foundational counterfactual theory, facilitating the extension of numerous theoretical results regarding the probability of causation to an individual granularity level and leading to a comprehensive set of theories on heterogeneous counterfactual bounds. Ultimately, our paper posits that if one acknowledges and wishes to leverage the ubiquitous heterogeneity, understanding causality as invariance across heterogeneous units, then DiscoSCM stands as a significant advancement in the methodology of counterfactual inference.

翻译：在因果推断领域，潜在结果（PO）与结构因果模型（SCM）被公认为两大主要框架。然而，当涉及第三层级评估（即与个体层面语义深度纠缠的反事实查询）时，这两个框架均因一致性规则引发的退化问题而面临局限。本文倡导将分布一致性结构因果模型（DiscoSCM）框架作为反事实推断的前沿方法，巧妙地融合了PO与SCM两者的优势。DiscoSCM框架独特地引入了单位选择变量$U$，并接纳了不可控外生噪声实现的概念。通过个性化激励场景，我们展示了PO与SCM框架在无退化条件下无法表征用户作为依从者（第三层级事件）概率的缺陷，而这一问题可通过采纳DiscoSCM中独立反事实噪声的假设得到妥善解决。这一创新性假设拓展了基础反事实理论，使得大量关于因果概率的理论结果能够推广至个体粒度层面，进而形成一套关于异质性反事实边界的完整理论。最终，本文提出：若研究者承认并希望利用普遍存在的异质性（即将因果理解为跨异质单位的恒常性），那么DiscoSCM将构成反事实推断方法论的重大进步。