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.

在非独立同分布数据的情景中，我们研究因果效应的估计，重点关注满足独立因果机制假设的可交换数据。为了弥补现有因果效应估计框架的不足，我们开发了针对可交换数据的广义框架，并引入了截断因式分解公式，以促进在我们的情景中对因果效应的识别和估计。为了说明潜在的应用，我们介绍了一个因果泊利亚球模型，并展示了干预在可交换数据设置中如何传播效应。最后，我们开发了一个算法，可以在多环境数据中进行同时因果发现和效应估计。