相对因果知识的可操作化：基于共享结果的私人报告的主干可识别性
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
- Imperial College London（帝国理工学院）
- Fifty One Degrees Ltd（51度公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文研究相对因果知识的主干可识别性，针对双智能体共同效应案例，明确局部因果边际无法唯一确定主干，提出需智能体传递因果识别的响应函数方可实现识别，以教育增值示例说明其兼具通信与政策组合属性。
AI中文摘要：
相对因果知识（Relativity of Causal Knowledge, RCK）解释了具有不同结构因果模型的智能体网络如何通过共享的干预一致抽象（即主干）交换因果知识。我们提出该传输机制所预设的先验识别问题：该主干何时由智能体的私人因果知识确定？在基本的双智能体共同效应案例中，两个私人原因影响一个共享结果，且每个智能体仅识别与其自身视角相关的单原因因果边际。我们证明，在标准兼容性、非退化性和局部重叠假设下，这些局部因果边际无法识别唯一的主干：无限多的联合干预核可诱导完全相同的私人报告，却在联合干预上存在分歧。随后我们给出一个条件恢复结果：可加性可分性消除了隐藏的交互自由度，但观测残差摘要仍不足；当智能体传递经因果识别的响应函数时，识别成为可能。一个教育增值示例说明，这首先是一个通信问题，而后才是政策组合问题。
英文摘要：
The Relativity of Causal Knowledge (RCK) explains how a network of agents with different structural causal models can exchange causal knowledge through a shared interventionally consistent abstraction, or backbone. We ask the prior identification question that this transport mechanism presupposes: when is that backbone determined by the agents' private causal knowledge? In the basic two-agent common-effect case, two private causes influence one shared outcome and each agent identifies only the single-cause causal marginal relevant to its own perspective. We show that, under standard compatibility, non-degeneracy, and local overlap assumptions, those local causal marginals do not identify a unique backbone. Infinitely many joint intervention kernels can induce exactly the same private reports while disagreeing on joint interventions. We then give a conditional recovery result. Additive separability removes the hidden interaction degree of freedom, but observational residual summaries remain insufficient. Identification becomes possible when agents communicate causally identified response functions. An education value-added example illustrates why this is first a communication problem, and only then a policy-composition problem.
