Janzing, Dominik

65 publications

CLeaR 2025 Cross-Validating Causal Discovery via Leave-One-Variable-Out Daniela Schkoda, Philipp Michael Faller, Dominik Janzing, Patrick Blöbaum

NeurIPS 2025 Root Cause Analysis of Outliers with Missing Structural Knowledge William Roy Orchard, Nastaran Okati, Sergio Hernan Garrido Mejia, Patrick Blöbaum, Dominik Janzing

AAAI 2025 Toward Falsifying Causal Graphs Using a Permutation-Based Test Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing

UAI 2025 Toward Universal Laws of Outlier Propagation Aram Ebtekar, Yuhao Wang, Dominik Janzing

NeurIPS 2024 Causal vs. Anticausal Merging of Predictors Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf, Dominik Janzing

MLOSS 2024 DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing

CLeaR 2024 Meaningful Causal Aggregation and Paradoxical Confounding Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing

AISTATS 2024 Quantifying Intrinsic Causal Contributions via Structure Preserving Interventions Dominik Janzing, Patrick Blöbaum, Atalanti A Mastakouri, Philipp M Faller, Lenon Minorics, Kailash Budhathoki

AISTATS 2024 Self-Compatibility: Evaluating Causal Discovery Without Ground Truth Philipp M. Faller, Leena C. Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing

NeurIPS 2023 Assumption Violations in Causal Discovery and the Robustness of Score Matching Francesco Montagna, Atalanti Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello

UAI 2023 Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt

AISTATS 2022 Obtaining Causal Information by Merging Datasets with MAXENT Sergio H. Garrido Mejia, Elke Kirschbaum, Dominik Janzing

AISTATS 2022 Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies Lenon Minorics, Caner Turkmen, David Kernert, Patrick Bloebaum, Laurent Callot, Dominik Janzing

UAI 2022 Causal Forecasting: Generalization Bounds for Autoregressive Models Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

ICML 2022 Causal Inference Through the Structural Causal Marginal Problem Luigi Gresele, Julius Von Kügelgen, Jonas Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing

ICML 2022 Causal Structure-Based Root Cause Analysis of Outliers Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing

CLeaR 2022 Cause-Effect Inference Through Spectral Independence in Linear Dynamical Systems: Theoretical Foundations Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf

ICML 2022 On Measuring Causal Contributions via Do-Interventions Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim

NeurIPSW 2022 Quantifying Causal Contribution in Rare Event Data Ali Caner Turkmen, Dominik Janzing, Oleksandr Shchur, Lenon Minorics, Laurent Callot

ICML 2022 Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello

ICLR 2022 You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction Osama Makansi, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

AISTATS 2021 Why Did the Distribution Change? Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng

AAAI 2021 A Theory of Independent Mechanisms for Extrapolation in Generative Models Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf

ICML 2021 Necessary and Sufficient Conditions for Causal Feature Selection in Time Series with Latent Common Causes Atalanti A Mastakouri, Bernhard Schölkopf, Dominik Janzing

AISTATS 2020 Feature Relevance Quantification in Explainable AI: A Causal Problem Dominik Janzing, Lenon Minorics, Patrick Bloebaum

NeurIPS 2019 Causal Regularization Dominik Janzing

NeurIPS 2019 Perceiving the Arrow of Time in Autoregressive Motion Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann

NeurIPS 2019 Selecting Causal Brain Features with a Single Conditional Independence Test per Feature Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing

AISTATS 2018 Cause-Effect Inference by Comparing Regression Errors Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

ICML 2018 Detecting Non-Causal Artifacts in Multivariate Linear Regression Models Dominik Janzing, Bernhard Schölkopf

AISTATS 2018 Group Invariance Principles for Causal Generative Models Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing

NeurIPS 2017 Avoiding Discrimination Through Causal Reasoning Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf

UAI 2017 Causal Consistency of Structural Equation Models Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

JMLR 2016 Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

UAI 2016 Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA Alexander Ihler, Dominik Janzing

ICML 2015 Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing

AISTATS 2015 Inference of Cause and Effect with Unsupervised Inverse Regression Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf

ICML 2015 Removing Systematic Errors for Exoplanet Search via Latent Causes Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

JMLR 2015 Semi-Supervised Interpolation in an Anticausal Learning Scenario Dominik Janzing, Bernhard Schölkopf

ICML 2015 Telling Cause from Effect in Deterministic Linear Dynamical Systems Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve

JMLR 2014 Causal Discovery with Continuous Additive Noise Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

ICML 2014 Consistency of Causal Inference Under the Additive Noise Model Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf

UAI 2014 Estimating Causal Effects by Bounding Confounding Philipp Geiger, Dominik Janzing, Bernhard Schölkopf

UAI 2014 Inferring Latent Structures via Information Inequalities Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, Bernhard Schölkopf

UAI 2014 Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction Co-Located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014 Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen

NeurIPS 2013 Causal Inference on Time Series Using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf

UAI 2013 From Ordinary Differential Equations to Structural Causal Models: The Deterministic Case Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

UAI 2013 Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

ICML 2012 On Causal and Anticausal Learning Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

UAI 2011 Detecting Low-Complexity Unobserved Causes Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf

UAI 2011 Identifiability of Causal Graphs Using Functional Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

UAI 2011 Kernel-Based Conditional Independence Test and Application in Causal Discovery Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf

NeurIPS 2011 On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

UAI 2011 Testing Whether Linear Equations Are Causal: A Free Probability Theory Approach Jakob Zscheischler, Dominik Janzing, Kun Zhang

COLT 2010 Causal Markov Condition for Submodular Information Measures Bastian Steudel, Dominik Janzing, Bernhard Schölkopf

AISTATS 2010 Identifying Cause and Effect on Discrete Data Using Additive Noise Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf

UAI 2010 Inferring Deterministic Causal Relations Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf

UAI 2010 Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery Kun Zhang, Bernhard Schölkopf, Dominik Janzing

NeurIPS 2010 Probabilistic Latent Variable Models for Distinguishing Between Cause and Effect Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

ICML 2010 Telling Cause from Effect Based on High-Dimensional Observations Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf

ICML 2009 Detecting the Direction of Causal Time Series Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf

UAI 2009 Identifying Confounders Using Additive Noise Models Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf

ICML 2009 Regression by Dependence Minimization and Its Application to Causal Inference in Additive Noise Models Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

NeurIPS 2008 Nonlinear Causal Discovery with Additive Noise Models Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf

ICML 2007 A Kernel-Based Causal Learning Algorithm Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu