In the field of causal modeling, potential outcomes (PO) and structural
causal models (SCMs) stand as the predominant frameworks. However, these
frameworks face notable challenges in practically modeling counterfactuals,
formalized as parameters of the joint distribution of potential outcomes.
Counterfactual reasoning holds paramount importance in contemporary
decision-making processes, especially in scenarios that demand personalized
incentives based on the joint values of $(Y(0), Y(1))$. This paper begins with
an investigation of the PO and SCM frameworks for modeling counterfactuals.
Through the analysis, we identify an inherent model capacity limitation, termed
as the ``degenerative counterfactual problem'', emerging from the consistency
rule that is the cornerstone of both frameworks. To address this limitation, we
introduce a novel \textit{distribution-consistency} assumption, and in
alignment with it, we propose the Distribution-consistency Structural Causal
Models (DiscoSCMs) offering enhanced capabilities to model counterfactuals. To
concretely reveal the enhanced model capacity, we introduce a new identifiable
causal parameter, \textit{the probability of consistency}, which holds
practical significance within DiscoSCM alone, showcased with a personalized
incentive example. Furthermore, we provide a comprehensive set of theoretical
results about the ``Ladder of Causation'' within the DiscoSCM framework. We
hope it opens new avenues for future research of counterfactual modeling,
ultimately enhancing our understanding of causality and its real-world
applications.

在因果建模领域，潜在结果和结构因果模型是主要框架。然而，这些框架在实际建模反事实时面临着明显的挑战，形式化表现为潜在结果的联合分布参数。本文通过对潜在结果和结构因果模型在建模反事实中的研究，提出了 “退化反事实问题” 这一内在模型容量限制，并引入了一种新的 “分布一致性” 假设，基于该假设提出了增强建模反事实能力的 “分布一致性结构因果模型”。通过引入可识别的因果参数 “一致性概率”，在个性化激励示例中展示了 DiscoSCM 在实践中的实际意义。此外，还在 DiscoSCM 框架中提供了关于 “因果阶梯” 的全面理论成果，希望为反事实建模的未来研究开辟新的方向，从而增强我们对因果关系及其在现实世界应用领域的理解。