论文标题
在稀疏机制转移假设下，异质环境中的因果发现
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
论文作者
论文摘要
机器学习方法通常取决于独立和相同分布（I.I.D.）数据的假设。但是，实际上，由于环境之间的分配变化，这种假设几乎总是违反。尽管可以通过变化分布的异质数据提供有价值的学习信号，但也众所周知，在任意（对抗）下的学习是不可能的。由于因果模型编码观察性和介入分布，因此因果关系为建模分布变化提供了有用的框架。在这项工作中，我们探讨了稀疏的机制移位假设，该假设认为分布变化是由于少量变化的因果条件而发生的。在这个想法的动机上，我们将其应用于从异质环境中学习因果结构，其中I.I.D.数据仅允许在没有限制性假设的情况下学习图形的等效类别。我们提出了机理移位评分（MSS），这是一种基于得分的方法，可适应各种经验估计量，如果稀疏的机制偏移假设成立，则可以证明具有很高概率的整个因果结构。从经验上讲，我们验证该理论预测的行为，并比较多个估计量和得分功能，以确定实践中最佳方法。与其他方法相比，我们展示了MSS如何通过非参数以及明确利用稀疏变化来弥合差距。
Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts between environments. Although valuable learning signals can be provided by heterogeneous data from changing distributions, it is also known that learning under arbitrary (adversarial) changes is impossible. Causality provides a useful framework for modeling distribution shifts, since causal models encode both observational and interventional distributions. In this work, we explore the sparse mechanism shift hypothesis, which posits that distribution shifts occur due to a small number of changing causal conditionals. Motivated by this idea, we apply it to learning causal structure from heterogeneous environments, where i.i.d. data only allows for learning an equivalence class of graphs without restrictive assumptions. We propose the Mechanism Shift Score (MSS), a score-based approach amenable to various empirical estimators, which provably identifies the entire causal structure with high probability if the sparse mechanism shift hypothesis holds. Empirically, we verify behavior predicted by the theory and compare multiple estimators and score functions to identify the best approaches in practice. Compared to other methods, we show how MSS bridges a gap by both being nonparametric as well as explicitly leveraging sparse changes.
