Distribution-consistency Structural Causal Models
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分布一致性结构因果模型
- 研究问题
- 如何在潜在结局和结构因果模型中表示联合反事实，并回应作者所称由一致性规则产生的模型容量限制及个性化激励需求？
- 方法与思路
- 作者提出分布一致性假设及 DiscoSCM 框架，引入一致性概率这一因果参数，并研究该框架内因果层级的理论性质。
- 材料陈述的贡献
- 摘要称新框架扩展反事实建模能力，提出在该框架中具有意义的可识别一致性概率，并以个性化激励示例展示用途。
- 阅读关注点
- 核对传统一致性与分布一致性的定义差异、所谓退化问题的论证、新参数的识别条件，以及新框架反事实语义的解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核验；摘要未给出定义和证明，所称传统框架限制及新参数可识别性均需全文核查。
原文摘要
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.