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stat.ME2026
Causal Discovery in Multivariate Extremes via Tail Asymmetry
Mengran Li, Daniela Castro-Camilo
Causal discovery in multivariate extremes is challenging because extreme observations are sparse, dependent, and often affected by latent common shocks. Existing approaches focus o…
stat.ME2026
Tail-Calibrated Estimation of Extreme Quantile Treatment Effects
Mengran Li, Daniela Castro-Camilo
Extreme quantile treatment effects (eQTEs) measure the causal impact of a treatment on the tails of an outcome distribution and are central for studying rare, high-impact events. S…
stat.AP2026
Probabilistic forecasting of weather-driven faults in electricity networks: a flexible approach for extreme and non-extreme events
Mateus Maia, Daniela Castro-Camilo, Jethro Browell
Electricity networks are vulnerable to weather damage, with severe events often leading to faults and power outages. Timely forecasts of fault occurrences, ranging from nowcasts to…