论文标题
因果关系：关于可交换数据中不变因果结构的识别
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
论文作者
论文摘要
基于约束的因果发现方法利用条件独立性测试来推断各种应用中的因果关系。正如大多数机器学习方法一样，现有工作着重于研究$ \ textIt {独立且相同分布式} $数据。但是，众所周知，即使使用无限的I.I.D。$ \ $数据，基于约束的方法也只能识别到宽Markov等效类的因果结构，从而对因果发现构成了基本限制。在这项工作中，我们观察到可交换的数据包含比I.I.D。$ \ $数据更丰富的条件独立性结构，并显示如何利用更丰富的结构来进行因果发现。我们首先提出了Finetti定理，该定理指出，具有某些非平凡条件独立性的可交换分布始终可以表示为$ \ textit {独立的因果机制（ICM）} $生成过程。然后，我们介绍了我们的主要可识别性定理，该定理表明从ICM生成过程中给定数据，可以通过执行条件独立性测试来识别其独特的因果结构。我们最终开发了一种因果发现算法，并证明了其适用于从多环境数据中推断因果关系的适用性。我们的代码和模型可在以下网址公开获取：https：//github.com/syguo96/causal-de-finetti
Constraint-based causal discovery methods leverage conditional independence tests to infer causal relationships in a wide variety of applications. Just as the majority of machine learning methods, existing work focuses on studying $\textit{independent and identically distributed}$ data. However, it is known that even with infinite i.i.d.$\ $ data, constraint-based methods can only identify causal structures up to broad Markov equivalence classes, posing a fundamental limitation for causal discovery. In this work, we observe that exchangeable data contains richer conditional independence structure than i.i.d.$\ $ data, and show how the richer structure can be leveraged for causal discovery. We first present causal de Finetti theorems, which state that exchangeable distributions with certain non-trivial conditional independences can always be represented as $\textit{independent causal mechanism (ICM)}$ generative processes. We then present our main identifiability theorem, which shows that given data from an ICM generative process, its unique causal structure can be identified through performing conditional independence tests. We finally develop a causal discovery algorithm and demonstrate its applicability to inferring causal relationships from multi-environment data. Our code and models are publicly available at: https://github.com/syguo96/Causal-de-Finetti
