Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
arXiv:2606.03251v1 Announce Type: cross Abstract: 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 im
The proliferation of large, real-world datasets and advances in causal AI present an opportunity to extract deeper, more reliable insights.
Improving causal feature selection can lead to more robust and explainable AI models, enhancing their reliability in critical applications.
The ability to systematically identify and utilize 'natural experiments' within existing datasets could significantly refine how AI learns from real-world phenomena.
- · Causal AI developers
- · Data scientists
- · AI ethics and safety researchers
- · Models relying solely on correlation
- · Organizations using less rigorous data analysis
- · AI applications with high-stakes decision-making based on spurious insights
AI models become more effective at identifying and leveraging causal relationships in complex systems.
Improved model trustworthiness leads to wider adoption of AI in domains requiring high reliability, such as healthcare and finance.
Enhanced understanding of causal mechanisms empowers more precise interventions and policy-making based on data-driven insights.
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Read at arXiv cs.LG