Causal discovery aims to learn causal relationships between variables from targeted data, making it a fundamental task in machine learning. However, causal discovery algorithms often rely on unverifiable causal assumptions, which are usually difficult to satisfy in real-world data, thereby limiting the broad application of causal discovery in practical scenarios. Inspired by these considerations, this work extensively benchmarks the empirical performance of various mainstream causal discovery algorithms, which assume i.i.d. data, under eight model assumption violations. Our experimental results show that differentiable causal discovery methods exhibit robustness under the metrics of Structural Hamming Distance and Structural Intervention Distance of the inferred graphs in commonly used challenging scenarios, except for scale variation. We also provide the theoretical explanations for the performance of differentiable causal discovery methods. Finally, our work aims to comprehensively benchmark the performance of recent differentiable causal discovery methods under model assumption violations, and provide the standard for reasonable evaluation of causal discovery, as well as to further promote its application in real-world scenarios.

翻译：因果发现旨在从观测数据中学习变量间的因果关系，是机器学习领域的基础任务。然而，因果发现算法通常依赖于无法验证的因果假设，这些假设在现实数据中往往难以满足，从而限制了因果发现在实际场景中的广泛应用。受此启发，本研究系统性地评估了多种主流因果发现算法（假设数据独立同分布）在八类模型假设违反情况下的实证性能。实验结果表明，除尺度变化外，在常用挑战性场景下，可微因果发现方法在推断图的结构汉明距离和结构干预距离指标上均表现出鲁棒性。我们同时为可微因果发现方法的性能提供了理论解释。最终，本研究旨在全面评估近期可微因果发现方法在模型假设违反下的性能，为因果发现的合理评估提供标准，并进一步推动其在真实场景中的应用。