发表机构
University of Washington; University of California San Francisco（华盛顿大学; 加州大学旧金山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对函数型因果发现缺乏统计推断的问题，提出一种基于假设检验的双变量因果发现框架，利用拟合优度和独立性检验及重抽样量化不确定性，并通过模拟和真实数据验证其有效性。
AI 中文摘要
因果发现方法旨在利用观测数据确定变量之间的因果方向。函数型因果发现方法依赖于结构和分布假设来确定方向性，但通常缺乏统计推断。本文从理论、软件和应用层面回顾了现有函数型因果发现方法的统计保证，并指出了一个关键缺口：缺乏一个可广泛适用于各类模型类的统一推断框架。作为弥补该缺口的第一步，我们通过在一个假设检验框架内重新利用拟合优度检验和独立性检验，形式化了一种针对双变量因果发现的基于检验的方法。由于方向性是通过两个不相交的假设检验确定的，对应四种因果发现结果，该方法提供了明确的量化不确定性和对假设违反的诊断性洞察。进一步使用重抽样来估计因果发现结果的比率，提供了额外的推断层。我们通过改变假设违反程度的模拟以及真实数据应用，展示了我们推断框架的使用和行为。最后，我们总结了实际经验和对推进函数型因果发现统计保证的建议。
英文摘要
Causal discovery methods aim to determine the causal direction between variables using observational data. Functional causal discovery methods rely on structural and distributional assumptions to determine directionality but typically lack statistical inference. This paper reviews the statistical guarantees of existing functional causal discovery methods in theory, software, and applied use, highlighting a key gap: the absence of a unified inferential framework that applies broadly across model classes. As a first step toward addressing this gap, we formalize a test-based approach for bivariate causal discovery by repurposing goodness-of-fit and independence tests within a hypothesis-testing framework. Because directionality is determined through two disjoint hypothesis tests, corresponding to four causal discovery outcomes, the approach provides explicit uncertainty quantification and diagnostic insight into assumption violations. Resampling is further used to estimate the rates of causal discovery outcomes, offering an additional layer of inference. We demonstrate the use and behavior of our inferential framework through simulations that vary the degree of assumption violation, as well as through real-data applications. We conclude with practical lessons and recommendations for advancing statistical guarantees in functional causal discovery.
