AI 中文总结
研究数字孪生在有反馈系统中预测干预响应时的验证与传输问题，针对平衡因果博弈给出相关条件，引入循环选择图推导标准，得出验证需结构假设，还为线性模型推导干预要求及刻画查询值范围并提供统计检验。
AI 中文摘要
数字孪生常用于预测系统对干预的响应。在有反馈的系统中，数字孪生必须再现平衡反事实，且在一个领域开发的数字孪生在机制变化后可能失效。我们研究这些预测何时可被验证和传输。对于平衡因果博弈，我们给出机制、平衡选择和干预设计的条件，在这些条件下与实验分布的一致性可识别感兴趣的反事实。我们展示了为何均值和协方差的一致性对于分布查询是不够的。然后我们引入循环选择图，并推导直接重用和结合不变源机制与目标信息的混合模型的标准。一个不可能性结果构建了在有限设计中的每个实验下都一致但在目标反事实上不一致的系统，表明验证需要结构假设。对于线性模型，我们推导依赖于变化的机制、观测模型和图支持的干预要求。当点识别失败时，我们刻画查询值的剩余范围。我们还为重构的均值和协方差提供统计检验，并在合成反馈系统中说明该理论。
英文摘要
Digital twins are often used to predict how a system would respond to an intervention. In systems with feedback, a twin must reproduce an equilibrium counterfactual, and a twin developed in one domain may fail after mechanisms change. We study when these predictions can be validated and transported. For equilibrium causal games, we give conditions on the mechanisms, equilibrium selection, and intervention design under which agreement with experimental distributions identifies the counterfactual of interest. We show why agreement of means and covariances is insufficient for distributional queries. We then introduce cyclic selection diagrams and derive criteria for direct reuse and for hybrid models that combine invariant source mechanisms with target information. An impossibility result constructs systems that agree under every experiment in a finite design but disagree on the target counterfactual, showing that validation requires structural assumptions. For linear models, we derive intervention requirements that depend on the mechanisms that changed, the observation model, and graph support. When point identification fails, we characterize the remaining range of query values. We also provide statistical tests for reconstructed means and covariances and illustrate the theory in synthetic feedback systems.
CommentsWorking Paper
