# The SfS-PhD Presentations

I was the host of the SfS-PhD presentations from 2021-2024. Here's what happened in the last four semesters:

### Spring 2024

- Julius von Kügelgen: "Backtracking Counterfactuals".
- Samuel Joray: "Recent Approaches to Instrumental Variable Estimation of Nonlinear Causal Effects”.
- Georgios Gavrilopulos: "Sequential Model Confidence Set”.
- Iris de Vries: "Past counterfactuals and future predictions - a climate scientist's attempt at using probability theory".
- Malte Londschien: "Weak Instruments: When the Music Stops".

### Autumn 2023

- Christoph Schultheiss: "On (causal) well-specification of regression estimates".
- Maybritt Schillinger: "Machine learning with unpaired samples – an example of high-dimensional climate projections".
- Felix Kuchelmeister: "Always reject H_0 when the data are separable?".
- Jakob Heiss: "How to forecast consistently based on noisy incomplete observations at irregular observation times".
- Florian Krach: "Path-Dependent Neural Jump Ordinary Differential Equations".
- Simon Briend (Pompeu Fabra & Paris Saclay): "Estimating Adam's Genealogy in Growing Recursive Trees".
- Cyrill Scheidegger: "Fitting high-dimensional additive models under hidden confounding".
- Mathieu Chevalley: "Causal Discovery in the Wild".
- Xinwei Shen: "Distribution is all you need? From generative AI to distributional regression".
- Sorawit Saengkyongam: "Identifying Representations for Intervention Extrapolation".

### Spring 2023

- Philipp Schmocker (NTU): "Global universal approximation with functional input neural networks on weighted spaces".
- Qikun Xiang (NTU): "Numerical methods for optimal transport and matching for teams".
- Jakob Heimer: "Thoughts on Visualization (in Statistical Consulting)".
- Felix Schur: "Meta Learning for Lifelong Sequential Decision Making".
- Florian Schwarb: "R-ICP: Invariant Causal Prediction under Random Effects".
- Jakob Heiss: "Bayesian Optimization-based Combinatorial Assignment".
- Sorawit Saengkyongam: "Effect-invariant mechanism for policy generalization".
- Felix Kuchelmeister: "Logistic Regression with small noise or few samples".
- Harald Besdziek: "Nearly parametric rate of the maximum likelihood estimator in mixtures of power series distributions".
- Maybritt Schillinger: "Simulating Regional and Global Climate Models with Machine Learning".
- Juan Gamella: "The Causal Room: datasets for causal discovery from well-understood physical mechanisms".
- Lea Tamberg: "Prediction of wellbeing outcomes based on indicators for basic human needs satisfaction".

### Autumn 2022

- Malte Londschien: "On the Generalized Method of Moments".
- Jeffrey Näf: "R-NL: high-dimensional covariance estimation when tails are heavy".
- Christoph Schultheiss: "Ancestor regression in linear structural equation models".
- Juan Gamella: "Validating Causal Discovery Algorithms: Realistic synthetic data with known ground truth".
- Jinzhou Li: "Root cause discovery in linear structural equation models with unknown causal ordering".
- Maybritt Schillinger: "Distribution Shifts in Climate Observations - Just a Mean Change?".
- Felix Kuchelmeister: "Linear Separability: From Dandelions to Logistic Regression".
- Jakob Heiss: "How Infinitely Wide Neural Networks Benefit from Multi-task Learning - an Exact Macroscopic Characterization".