36 citations · 74 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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.…
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…
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…