activity
20182022
most citedFeature relevance quantification in explainable AI: A causal problem

156 citations · 181 across the 4 of their papers we have counts for

collaborators

6 papers

stat.ME20222 cited

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies

Lenon Minorics, Caner Turkmen, David Kernert +3

This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and sh…

stat.ML2022

Correcting Confounding via Random Selection of Background Variables

You-Lin Chen, Lenon Minorics, Dominik Janzing

We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of…

stat.ML201923 cited

Causal structure based root cause analysis of outliers

Dominik Janzing, Kailash Budhathoki, Lenon Minorics +1

We describe a formal approach to identify 'root causes' of outliers observed in variables in a scenario where the causal relation between the variables is a kno…

stat.ML2019156 cited

Feature relevance quantification in explainable AI: A causal problem

Dominik Janzing, Lenon Minorics, Patrick Blöbaum

We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one…

math.SP2019

Eigenvalue Approximation for Krein-Feller-Operators

Uta Freiberg, Lenon Minorics

We study the limiting behavior of the eigenvalues of Krein-Feller-Operators with respect to weakly convergent probability measures. Therefore, we give a representation of the eigen…

math.SP2018

Spectral Asymptotics for Krein-Feller-Operators with respect to -Variable Cantor Measures

Lenon Alexander Minorics

We study the limiting behavior of the Dirichlet and Neumann eigenvalue counting function of generalized second order differential operators , where