As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect common preconceptions about generating processes regarding noise distributions, model classes, and more. In this work, we propose a novel method for falsifying the output of a causal discovery algorithm in the absence of ground truth. Our key insight is that while statistical learning seeks stability across subsets of data points, causal learning should seek stability across subsets of variables. Motivated by this insight, our method relies on a notion of compatibility between causal graphs learned on different subsets of variables. We prove that detecting incompatibilities can falsify wrongly inferred causal relations due to violation of assumptions or errors from finite sample effects. Although passing such compatibility tests is only a necessary criterion for good performance, we argue that it provides strong evidence for the causal models whenever compatibility entails strong implications for the joint distribution. We also demonstrate experimentally that detection of incompatibilities can aid in causal model selection.

翻译：由于因果事实极其稀缺，因果发现算法通常仅在模拟数据上评估。这一现状令人担忧，因为模拟过程往往反映了对数据生成机制（如噪声分布、模型类别等）的既有预设。本文提出一种无需真实因果关系即可验证因果发现算法输出的新方法。我们的核心洞察在于：统计学习追求数据点子集间的稳定性，而因果学习应追求变量子集间的稳定性。基于此观点，该方法依赖于在不同变量子集上学习得到的因果图之间的兼容性概念。我们证明，检测不兼容性可以识别因假设违背或有限样本效应导致的错误因果推断。尽管通过此类兼容性检验仅是取得良好性能的必要条件，但当兼容性对联合分布具有强约束作用时，它能为因果模型提供有力证据。实验结果表明，不兼容性检测有助于因果模型选择。