Research

Causal Discovery in Extremes

Developing novel causal inference algorithms tailored for extreme value regimes, currently focusing on theoretical robustness and validation using extensive simulation studies.

Extreme quantile treatment effect (EQTE)

Extreme quantile treatment effects (eQTEs) measure causal impacts on outcome distribution tails. We propose the TIEE framework combining information across quantile levels with extreme value models, enabling causal inference for rare, high-impact events in environmental risk, economics, and public health.

Extreme Event Attribution (EEA)

Exploring how tail assumptions influence attribution of extreme climate events, combining simulation, spatial methodology, and software development.

Wee Extremes: EVA 2023 Data Challenge

Statistical Methodology for Extreme Event Risk Assessment in the EVA 2023 Data Challenge.