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Name: Ngoc Tran 

Talk Title: Causal inference for extremes on river networks

Abstract: Causal inference for extreme aims to discover cause and effect relation between large observed values of random variables. This is a fundamental problem to in many applications, from finance, engineering risks, nutrition to hydrology, to name a few. Unique challenges to extreme values are lack of data and lack of model smoothness due to the max operator. Existing methods in extreme value statistics for dimensions d ≥ 3 are limited due to an intractable likelihood, while techniques for learning Bayesian networks require a large amount of data to fit nonlinear models. This talk showcases the max-linear model and new algorithms for fitting them. Our method performs well on real data, recovering a directed graph for both the Danube and the Lower Colorado with high accuracy purely through extreme measurements. We also compare our method to state-of-the-art algorithms for causal inference for nonlinear models, and outline open problems in hydrology, extreme value statistics and machine learning.

This talk is an invited talk at EVA 2021. View the programme here.