梳理真实世界因果发现数据，推动方法落地应用
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
- 系统分析生物、神经科学等领域的实际因果数据
- 指出当前方法多基于不切实际假设，评估数据过于简单
- 呼吁采用真实数据与更合理指标，促进方法实用化
因果发现旨在从数据中自动揭示因果关系，具有在多个科学领域广泛应用的潜力。然而其实际应用仍受限。现有方法常依赖不现实假设，且仅在简单合成数据集上评估，评估指标也往往不足。本文通过系统回顾近期因果发现文献，验证了上述问题。我们展示了生物学、神经科学和地球科学等领域中的实际应用场景，这些领域中因果发现有望解决关键挑战。文中梳理了来自这些领域的仿真与真实数据集，并讨论了常见假设违背现象，这些现象催生了新方法的发展。目标是推动研究社区采用更优的评估实践，使用真实数据与更充分的评估指标。
原文摘要 · Abstract (English)
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.
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