Patrick Blöbaum

Elke Kirschbaum

CoRR, 2023

Beyond Single-Feature Importance with ICECREAM.

[BibT_eX]

[DOI]

Michael Oesterle

Atalanti-Anastasia Mastakouri

Elke Kirschbaum

CoRR, 2023

Toward Falsifying Causal Graphs Using a Permutation-Based Test.

[BibT_eX]

[DOI]

Elias Eulig

Atalanti-Anastasia Mastakouri

Michaela Hardt

CoRR, 2023

Interventional and Counterfactual Inference with Diffusion Models.

[BibT_eX]

[DOI]

Patrick Chao

CoRR, 2023

Sequential Kernelized Independence Testing.

[BibT_eX]

[DOI]

Aleksandr Podkopaev

Aaditya Ramdas

Proceedings of the International Conference on Machine Learning, 2023

Thompson Sampling with Diffusion Generative Prior.

[BibT_eX]

[DOI]

Yu-Guan Hsieh

Branislav Kveton

Proceedings of the International Conference on Machine Learning, 2023

Manifold Restricted Interventional Shapley Values.

[BibT_eX]

[DOI]

Muhammad Faaiz Taufiq

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models.

[BibT_eX]

[DOI]

Atalanti-Anastasia Mastakouri

Peter Götz

Kailash Budhathoki

CoRR, 2022

On Measuring Causal Contributions via do-interventions.

[BibT_eX]

[DOI]

Yonghan Jung

Proceedings of the International Conference on Machine Learning, 2022

Causal structure-based root cause analysis of outliers.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Why did the distribution change?

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Quantifying causal contribution via structure preserving interventions.

[BibT_eX]

[DOI]

CoRR, 2020

Feature relevance quantification in explainable AI: A causal problem.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Analysis of cause-effect inference by comparing regression errors.

[BibT_eX]

[DOI]

PeerJ Comput. Sci., 2019

Feature relevance quantification in explainable AI: A causality problem.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Analysis of Cause-Effect Inference via Regression Errors.

[BibT_eX]

[DOI]

CoRR, 2018

Cause-Effect Inference by Comparing Regression Errors.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Estimation of interventional effects of features on prediction.

[BibT_eX]

[DOI]

Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

A novel principle for causal inference in data with small error variance.

[BibT_eX]

[DOI]

Takashi Washio

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

Error Asymmetry in Causal and Anticausal Regression.

[BibT_eX]

[DOI]

Takashi Washio

CoRR, 2016

2015

Discriminative and Generative Models in Causal and Anticausal Settings.

[BibT_eX]

[DOI]

Takashi Washio

Proceedings of the Advanced Methodologies for Bayesian Networks, 2015

Unsupervised Dimensionality Reduction for Transfer Learning.

[BibT_eX]

[DOI]