Tom Hochsprung — accepted AI-conference papers

2024

A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs

Tom Hochsprung, Jakob Runge, Andreas Gerhardus

UAI 2024poster

Structural Causal Models (SCMs) are an important tool in causal inference. They induce a graph and if the graph is acyclic, a unique observational distribution. A standard result states that in this acyclic case, the induced observational distribution satisfies a d-separation global Markov property…

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2023

Increasing effect sizes of pairwise conditional independence tests between random vectors

Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge

UAI 2023poster

A simple approach to test for conditional independence of two random vectors given a third random vector is to simultaneously test for conditional independence of every pair of components of the two random vectors given the third random vector. In this work, we show that conditioning on additional c…

Cited by 3 Source ↗PDF ↗Scholar ↗