When domain knowledge is limited and experimentation is restricted by
ethical, financial, or time constraints, practitioners turn to observational
causal discovery methods to recover the causal structure, exploiting the
statistical properties of their data. Because causal discovery without further
assumptions is an ill-posed problem, each algorithm comes with its own set of
usually untestable assumptions, some of which are hard to meet in real
datasets. Motivated by these considerations, this paper extensively benchmarks
the empirical performance of recent causal discovery methods on observational
i.i.d. data generated under different background conditions, allowing for
violations of the critical assumptions required by each selected approach. Our
experimental findings show that score matching-based methods demonstrate
surprising performance in the false positive and false negative rate of the
inferred graph in these challenging scenarios, and we provide theoretical
insights into their performance. This work is also the first effort to
benchmark the stability of causal discovery algorithms with respect to the
values of their hyperparameters. Finally, we hope this paper will set a new
standard for the evaluation of causal discovery methods and can serve as an
accessible entry point for practitioners interested in the field, highlighting
the empirical implications of different algorithm choices.

本研究对观测因果发现方法的实证表现进行了广泛的基准测试，发现基于分数匹配的方法在具有挑战性的情景中推断图的假阳性和假阴性率方面表现出令人惊讶的性能，并提供了理论洞察。该研究还首次尝试基于超参数值评估因果发现算法的稳定性。希望这篇论文能够为因果发现方法的评估树立新标准，并成为对该领域感兴趣的实践者的可访问入口，突出了不同算法选择的实证影响。