Research Group "Stochastic Algorithms and Nonparametric Statistics"
Research Seminar "Mathematical Statistics" Summer Semester 2026
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| 22.04.2026 | Eddie Aamari & Arthur Stephanovich (Paris) |
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| 29.04.2026 | Nicola Gnecco (Imperial College London) |
| Extremes of structural causal models The behaviour of extreme observations is well-understood for time series or spatial data, but little is known if the data generating process is a structural causal model (SCM). We study the behavior of extremes in this model class, both for the observational distribution and under extremal interventions. We show that under suitable regularity conditions on the structure functions, the extremal behavior is described by a multivariate Pareto distribution, which can be represented as a new SCM on an extremal graph. Importantly, the latter is a sub-graph of the graph in the original SCM, which means that causal links can disappear in the tails. We further introduce a directed version of extremal graphical models and show that an extremal SCM satisfies the corresponding Markov properties. Based on a new test of extremal conditional independence, we propose two algorithms for learning the extremal causal structure from data. The first is an extremal version of the PC-algorithm, and the second is a pruning algorithm that removes edges from the original graph to consistently recover the extremal graph. The methods are illustrated on river data with known causal ground truth. Organiser: Katarzyna Reluga |
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| 06.05.2026 | Vladimir Spokoiny (WIAS Berlin) | |
| 13.05.2026 | Holger Dette (RUB) |
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| 20.05.2026 | |
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| 27.05.2026 | Ester Mariucci (Université de Versailles Saint Quentin, Paris Saclay) |
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| 03.06.2026 | |
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| 10.06.2026 | Michael Sørensen (University of Copenhagen) |
| 17.06.2026 | |
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| 24.06.2026 | |
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| 01.07.2026 | |
| >HVP 11 a, R.313 | |
| 08.07.2026 | Gilles Blanchard (Université Paris Saclay) |
| HVP 11 a, R.313 | |
last reviewed: March 20, 2026 by Christine Schneider