Publications (18)
Skewness-Robust Causal Discovery in Location-Scale Noise Models
Daniel Klippert, Alexander Marx
The paper introduces SkewD, a likelihood‑based method for bivariate causal discovery that works with location‑scale noise models even when the noise distribution is skewed, improvi…
Formally Justifying MDL-based Inference of Cause and Effect
Alexander Marx, Jilles Vreeken
The algorithmic independence of conditionals, which postulates that the causal mechanism is algorithmically independent of the cause, has recently inspired many highly successful a…
On the Properties and Estimation of Pointwise Mutual Information Profiles
PaweŠCzyż, Frederic Grabowski, Julia E. Vogt +2
The pointwise mutual information profile, or simply profile, is the distribution of pointwise mutual information for a given pair of random variables. One of its important properti…
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Alexander Immer, Christoph Schultheiss, Julia E. Vogt +3
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect can be written as a function of the cause and a noise source independe…
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
Mirco Mutti, Riccardo De Santi, Marcello Restelli +2
Posterior sampling allows exploitation of prior knowledge on the environment's transition dynamics to improve the sample efficiency of reinforcement learning. The prior is typicall…
Beyond Normal: On the Evaluation of Mutual Information Estimators
PaweŠCzyż, Frederic Grabowski, Julia E. Vogt +2
Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.…