Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.

翻译：因果发现旨在从数据中自动揭示因果关系，这一能力在许多科学领域具有巨大潜力。然而，其在现实世界中的应用仍然有限。当前方法通常依赖于不切实际的假设，并且仅在简单的合成玩具数据集上进行评估，评估指标也往往不够充分。本文通过对近期因果发现文献的系统性综述，证实了这些观点。我们展示了在生物学、神经科学和地球科学领域的应用——这些领域中因果发现有望解决关键挑战。我们重点介绍了这些领域中可用的模拟和真实世界数据集，并讨论了促使新方法开发的常见假设违反情况。我们的目标是通过利用更真实的数据集和更恰当的评估指标，鼓励研究社区采用更好的评估实践。