36 citations · 74 across the 14 of their papers we have counts for
14 papers
Skewness-Robust Causal Discovery in Location-Scale Noise Models
Daniel Klippert, Alexander Marx
To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model. Crucially, we need to be able to distinguish cause from ef…
On the Properties and Estimation of Pointwise Mutual Information Profiles
Paweł Czyż, 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…
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
Paweł Czyż, 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.…
Identifiability Results for Multimodal Contrastive Learning
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo +2
Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effecti…
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…
Estimating Mutual Information via Geodesic NN
Alexander Marx, Jonas Fischer
Estimating mutual information (MI) between two continuous random variables and allows to capture non-linear dependencies between them, non-parametrically. As such, MI estim…
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…
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms
Alexander Marx, Lincen Yang, Matthijs van Leeuwen
Estimating conditional mutual information (CMI) is an essential yet challenging step in many machine learning and data mining tasks. Estimating CMI from data that contains both dis…
A Weaker Faithfulness Assumption based on Triple Interactions
Alexander Marx, Arthur Gretton, Joris M. Mooij
One of the core assumptions in causal discovery is the faithfulness assumption, i.e., assuming that independencies found in the data are due to separations in the true causal graph…
Testing Conditional Independence on Discrete Data using Stochastic Complexity
Alexander Marx, Jilles Vreeken
Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independen…
Causal Discovery by Telling Apart Parents and Children
Alexander Marx, Jilles Vreeken
We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three m…
Telling Cause from Effect using MDL-based Local and Global Regression
Alexander Marx, Jilles Vreeken
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables and from observational data. The two-variable case is…
Causal Inference on Multivariate and Mixed-Type Data
Alexander Marx, Jilles Vreeken
Given data over the joint distribution of two random variables and , we consider the problem of inferring the most likely causal direction between and . In particular…