多主体因果知识如何通过共享结果反推共同因果结构？
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
- 基于局部因果边际推断共享因果骨架，但存在无限多种可能
- 仅靠观测数据无法唯一确定联合干预机制，需额外通信
- 若主体共享已识别的响应函数，即可恢复唯一因果骨架
相对因果知识（RCK）解释了拥有不同结构因果模型的智能体如何通过一个干预一致的抽象框架——即骨干——交换因果知识。本文探讨该传输机制所依赖的前提问题：何时能由各主体的私有因果知识唯一确定这一骨干？在基础的双主体共因情形下，两个私有原因影响同一共享结果，每个主体仅能识别其自身视角下的单因因果边际。我们证明，在标准兼容性、非退化与局部重叠假设下，这些局部因果边际无法唯一确定骨干；存在无穷多个联合干预核可产生完全相同的私有报告，却对联合干预存在分歧。随后给出一个条件可恢复结果：可加分离性消除了隐藏交互自由度，但观测残差总结仍不足。当主体间通信已识别的响应函数时，识别才成为可能。教育增值分析案例说明，这首先是一个通信问题，其次才是政策组合问题。
原文摘要 · Abstract (English)
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.
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