发现真实数据中存在自然实验，可提升模型性能。
Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
- 用因果发现识别数据中的自然实验
- 实验证明利用自然实验能提升模型表现
- 适合研究因果推断与真实数据的学者
自然界中，某些事件只影响部分个体或群体，构成隐含干预，称为自然实验。例如，新冠疫情是病毒对感染人群的干预。本文探讨现有真实世界数据集中是否存在自然实验，并研究如何利用它们。通过因果发现恢复潜在因果图，并基于因果关系进行特征选择；若将数据视为干预数据而非观察数据后下游性能提升，则表明数据中存在自然实验。首先在合成图上验证该方法有效性，随后在大量真实数据集上系统评估。结果表明，真实数据集确实包含自然实验，且可通过因果推断加以利用以提升模型性能。本工作为该领域首次探索，在有限范围内提供初步分析。
原文摘要 · Abstract (English)
In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments. For example, the COVID-19 pandemic was an intervention by the coronavirus on the sub-population infected with COVID. We ask, do natural experiments occur in existing real-world datasets? If yes, how should we treat them? To detect natural experiments in data, we use causal discovery to recover the underlying causal graph and perform feature selection based on causal links. If downstream performance improves by treating the data as interventional rather than observational, we argue that this suggests the dataset contains natural experiments. We first validate this hypothesis by simulating datasets with and without natural experiments using synthetic graphs. We then perform a systematic empirical evaluation on a large suite of real-world datasets. Our results indicate that real-world datasets do contain natural experiments and we can take advantage of those natural experiments to improve model performance using causal inference. Our work represents the initial foray into this area, offering a preliminary exploration within a limited scope.
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