Author

S. Weichwald

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Preprint Oct 2026

Algorithmics for Graphical Separation Criteria

Statistical graphical models are representations of the dependences between the components of a random vector. They encode these dependences via graphical separation criteria. We consider the algorithmic problem of checking separation statements for the various graph classes and separation criteria that have been devel...

Moisés Chavira-Flores, S. Weichwald, Leonard Henckel · 0 citations

#machine learning Apr 2026

Identifying Causal Effects Using a Single Proxy Variable

Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under an assum...

Silvan Vollmer, Niklas Pfister, S. Weichwald · arXiv.org · 0 citations

#machine learning Open access Jun 2023

Unfair utility functions and first steps towards improving them

This work suggests that fairness can be directly analyzed as a property of the utility function, and suggests modifying utility functions such that they satisfy value of information fairness.

F. H. Jørgensen, S. Weichwald, J. Peters · Journal of Causal Inference · 0 citations