The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes
数字孪生反事实框架:模拟潜在结果的验证架构
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…