arXiv:2605.27474v1 Announce Type: cross Abstract: Estimating how an outcome responds to a continuous treatment (the Average Dose-Response Function, or ADRF) is a core causal-inference primitive. However, when outcomes possess heavy tails, standard robust double machine learning (DML) deliberately suppresses these extremes to stabilize the bulk average. In high-stakes settings, such as financial returns or climate losses, this omitted 1-in-1000 extreme event is the actual target quantity. Furthermore, current methods that read the tail from a model's residuals suffer from circular dependence, c
The increasing focus on applying AI/ML to high-stakes scenarios with extreme events, such as climate modeling and financial risk, is highlighting limitations in current causal inference methods.
Accurate prediction and understanding of extreme events are critical for robust decision-making in finance, climate policy, and disaster preparedness, which current 'robust' methods often suppress.
Current causal inference methods, particularly in ML, typically omit or suppress extreme outcomes; this new approach seeks to directly model and understand these critical 'fat tail' events.
- · Financial risk management
- · Climate modeling
- · Insurance and reinsurance
- · High-stakes AI applications
- · Traditional 'robust' statistical methods
- · Models that underpredict extreme events
Improved early warning systems and risk assessment for catastrophic events.
More reliable policy interventions designed to mitigate the impact of rare, high-consequence occurrences.
A shift in regulatory frameworks demanding extreme event modeling as a standard for critical infrastructure and finance.
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Read at arXiv cs.LG