We study causal effect estimation in a setting where the data are not i.i.d. (independent and identically distributed). We focus on exchangeable data satisfying an assumption of independent causal mechanisms. Traditional causal effect estimation frameworks, e.g., relying on structural causal models and do-calculus, are typically limited to i.i.d. data and do not extend to more general exchangeable generative processes, which naturally arise in multi-environment data. To address this gap, we develop a generalized framework for exchangeable data and introduce a truncated factorization formula that facilitates both the identification and estimation of causal effects in our setting. To illustrate potential applications, we introduce a causal P\'olya urn model and demonstrate how intervention propagates effects in exchangeable data settings. Finally, we develop an algorithm that performs simultaneous causal discovery and effect estimation given multi-environment data.

翻译：本研究探讨非独立同分布数据背景下的因果效应估计问题，重点关注满足独立因果机制假设的可交换数据。传统因果效应估计框架（例如基于结构因果模型与do-演算的方法）通常局限于独立同分布数据，难以推广至多环境数据中自然出现的更广义可交换生成过程。为填补此理论空白，我们构建了面向可交换数据的广义框架，并提出一种截断分解公式，该公式能同时支持本研究场景下因果效应的识别与估计。为展示潜在应用，我们引入因果Pólya瓮模型，并论证干预措施如何在可交换数据环境中传导效应。最后，我们开发了一种能在多环境数据条件下同步实现因果发现与效应估计的算法。