The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes
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数字孪生反事实框架：模拟潜在结果的验证架构
- 研究问题
- 如何评估数字孪生模拟的反事实结果，并区分可通过可观测数据检验的边际因果主张与依赖个体联合分布的主张？
- 方法与思路
- 作者在潜在结果框架中定义随机模拟器，提出分层保真度假设与五级验证架构，并提供界限、敏感性和不确定性分析。
- 材料陈述的贡献
- 摘要提出边际验证与 copula 依赖的正式区分，明确个体效应分布等对象仍依赖不可观测的个体内潜在结果关联。
- 阅读关注点
- 应核对各级保真度假设对应的目标量、可观测验证的覆盖范围，以及联合因果主张如何随 copula 假设改变。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；可观测数据上的验证不能直接验证个体反事实，摘要也承认这一边界。
原文摘要
The fundamental problem of causal inference - that the counterfactual outcome for any individual is never observed - has shaped the entire methodology of the field. Every existing approach substitutes assumptions for missing data: ignorability, parallel trends, exclusion restrictions. None produces the counterfactual itself. This paper proposes the Digital Twin Counterfactual Framework (DTCF): rather than estimating the counterfactual statistically, we simulate it using a digital twin and subject the simulation to a hierarchical validation regime. We formalize the digital twin simulator as a stochastic mapping within the potential outcomes framework and introduce a hierarchy of twin fidelity assumptions - from marginal fidelity through joint fidelity to structural fidelity - each unlocking a progressively richer class of estimands. The central contribution is threefold. First, a five-level validation architecture converts the unfalsifiable claim that the simulator produces correct counterfactuals into falsifiable tests against observable data. Second, a formal decomposition separates causal quantities into those that are marginally validated (ATE, CATE, QTE - testable through observable-arm comparison) and those that are copula-dependent (the ITE distribution, probability of benefit/harm, variance of treatment effects - permanently reliant on the unobservable within-individual dependence structure). Third, bounding, sensitivity, and uncertainty quantification tools make the copula dependence explicit. The DTCF does not resolve the fundamental problem of causal inference. What it provides is a framework in which marginal causal claims become increasingly testable, joint causal claims become explicitly assumption-indexed, and the gap between the two is formally characterized.