Dominik Janzing

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 60

Top Venues

CoRR

ICML

AISTATS

UAI

Publications

Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. AISTATS (2024)
Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing
Meaningful Causal Aggregation and Paradoxical Confounding. CLeaR (2024)
Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman
Multiply-Robust Causal Change Attribution. CoRR (2024)
Nastaran Okati, Sergio Hernan Garrido Mejia, William Roy Orchard, Patrick Blöbaum, Dominik Janzing
Root Cause Analysis of Outliers with Missing Structural Knowledge. CoRR (2024)
Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki
Quantifying intrinsic causal contributions via structure preserving interventions. AISTATS (2024)
Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
Assumption violations in causal discovery and the robustness of score matching. CoRR (2023)
Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Mila Hardt
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts. CoRR (2023)
Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt
Causal information splitting: Engineering proxy features for robustness to distribution shifts. UAI (2023)
Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing
Toward Falsifying Causal Graphs Using a Permutation-Based Test. CoRR (2023)
Dominik Janzing, Philipp Michael Faller, Leena Chennuru Vankadara
Reinterpreting causal discovery as the task of predicting unobserved joint statistics. CoRR (2023)
Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
Assumption violations in causal discovery and the robustness of score matching. NeurIPS (2023)
Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing
Meaningful Causal Aggregation and Paradoxical Confounding. CoRR (2023)
Numair Sani, Atalanti-Anastasia Mastakouri, Dominik Janzing
Bounding probabilities of causation through the causal marginal problem. CoRR (2023)
Philipp Michael Faller, Leena Chennuru Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. CoRR (2023)
Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing
Causal structure-based root cause analysis of outliers. ICML (2022)
Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
On Measuring Causal Contributions via do-interventions. ICML (2022)
Kailash Budhathoki, George Michailidis, Dominik Janzing
Explaining the root causes of unit-level changes. CoRR (2022)
Lenon Minorics, Ali Caner Türkmen, David Kernert, Patrick Blöbaum, Laurent Callot, Dominik Janzing
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies. AISTATS (2022)
Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML (2022)
Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello
Score matching enables causal discovery of nonlinear additive noise models. CoRR (2022)
Dominik Janzing, Sergio Hernan Garrido Mejia
Phenomenological Causality. CoRR (2022)
Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
Causal forecasting: generalization bounds for autoregressive models. UAI (2022)
Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. CLeaR (2022)
You-Lin Chen, Lenon Minorics, Dominik Janzing
Correcting Confounding via Random Selection of Background Variables. CoRR (2022)
Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
Causal Inference Through the Structural Causal Marginal Problem. CoRR (2022)
Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. CoRR (2022)
Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR (2022)
Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
Causal Inference Through the Structural Causal Marginal Problem. ICML (2022)
Sergio H. Garrido Mejia, Elke Kirschbaum, Dominik Janzing
Obtaining Causal Information by Merging Datasets with MAXENT. AISTATS (2022)
Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf
A Theory of Independent Mechanisms for Extrapolation in Generative Models. AAAI (2021)
Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing
Necessary and sufficient conditions for causal feature selection in time series with latent common causes. ICML (2021)
Dominik Janzing
Causal version of Principle of Insufficient Reason and MaxEnt. CoRR (2021)
Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng
Why did the distribution change? AISTATS (2021)
Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng
Why did the distribution change? CoRR (2021)
Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR (2021)
Leena Chennuru Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
Causal Forecasting: Generalization Bounds for Autoregressive Models. CoRR (2021)
Dominik Janzing, Lenon Minorics, Patrick Blöbaum
Feature relevance quantification in explainable AI: A causal problem. AISTATS (2020)
Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf
A theory of independent mechanisms for extrapolation in generative models. CoRR (2020)
Dominik Janzing, Patrick Blöbaum, Lenon Minorics
Quantifying causal contribution via structure preserving interventions. CoRR (2020)
Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing
Selecting causal brain features with a single conditional independence test per feature. NeurIPS (2019)
Dominik Janzing
Causal Regularization. CoRR (2019)
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. 5 (2019)
Dominik Janzing
Causal Regularization. NeurIPS (2019)
Dominik Janzing, Lenon Minorics, Patrick Blöbaum
Feature relevance quantification in explainable AI: A causality problem. CoRR (2019)
Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann
Perceiving the arrow of time in autoregressive motion. NeurIPS (2019)
Dominik Janzing, Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum
Causal structure based root cause analysis of outliers. CoRR (2019)
Dominik Janzing, Bernhard Schölkopf
Detecting non-causal artifacts in multivariate linear regression models. ICML (2018)
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Analysis of Cause-Effect Inference via Regression Errors. CoRR (2018)
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Cause-Effect Inference by Comparing Regression Errors. AISTATS (2018)
Dominik Janzing, Pawel Wocjan
Does Universal Controllability of Physical Systems Prohibit Thermodynamic Cycles? Open Syst. Inf. Dyn. 25 (3) (2018)
Dominik Janzing, Bernhard Schölkopf
Detecting non-causal artifacts in multivariate linear regression models. CoRR (2018)
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing
Group invariance principles for causal generative models. AISTATS (2018)
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing
Group invariance principles for causal generative models. CoRR (2017)
Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
Causal Consistency of Structural Equation Models. CoRR (2017)
Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
Causal Consistency of Structural Equation Models. UAI (2017)
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
Avoiding Discrimination through Causal Reasoning. NIPS (2017)
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
Avoiding Discrimination through Causal Reasoning. CoRR (2017)
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. 17 (2016)
Moritz Grosse-Wentrup, Dominik Janzing, Markus Siegel, Bernhard Schölkopf
Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach. NeuroImage 125 (2016)
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA 113 (27) (2016)
Dominik Janzing, Bernhard Schölkopf
Semi-supervised interpolation in an anticausal learning scenario. J. Mach. Learn. Res. 16 (2015)
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Removing systematic errors for exoplanet search via latent causes. ICML (2015)
Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, Dominik Janzing
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML (2015)
Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve
Telling cause from effect in deterministic linear dynamical systems. ICML (2015)
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Removing systematic errors for exoplanet search via latent causes. CoRR (2015)
Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve
Telling cause from effect in deterministic linear dynamical systems. CoRR (2015)
Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf
Inference of Cause and Effect with Unsupervised Inverse Regression. AISTATS (2015)
Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf
Consistency of Causal Inference under the Additive Noise Model. ICML (2014)
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15 (1) (2014)
Philipp Geiger, Dominik Janzing, Bernhard Schölkopf
Estimating Causal Effects by Bounding Confounding. UAI (2014)
Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR (2014)
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
Distinguishing cause from effect using observational data: methods and benchmarks. CoRR (2014)
Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, Bernhard Schölkopf
Inferring latent structures via information inequalities. UAI (2014)
Katja Ried, Megan Agnew, Lydia Vermeyden, Dominik Janzing, Robert W. Spekkens, Kevin J. Resch
Inferring causal structure: a quantum advantage. CoRR (2014)
Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf
Consistency of Causal Inference under the Additive Noise Model. CoRR (2013)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference (2013)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal Inference on Time Series using Restricted Structural Equation Models. NIPS (2013)
Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
From Ordinary Differential Equations to Structural Causal Models: the deterministic case CoRR (2013)
Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. CoRR (2013)
Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI (2013)
Jan Lemeire, Dominik Janzing
Replacing Causal Faithfulness with Algorithmic Independence of Conditionals. Minds Mach. 23 (2) (2013)
Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI (2013)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal Inference on Time Series using Structural Equation Models CoRR (2012)
Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf
Information-geometric approach to inferring causal directions. Artif. Intell. (2012)
Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
Identifying confounders using additive noise models CoRR (2012)
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
Identifiability of Causal Graphs using Functional Models CoRR (2012)
Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
Inferring deterministic causal relations CoRR (2012)
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Kernel-based Conditional Independence Test and Application in Causal Discovery CoRR (2012)
Jakob Zscheischler, Dominik Janzing, Kun Zhang
Testing whether linear equations are causal: A free probability theory approach CoRR (2012)
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
Detecting low-complexity unobserved causes CoRR (2012)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
On causal and anticausal learning. ICML (2012)
Kun Zhang, Bernhard Schölkopf, Dominik Janzing
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery CoRR (2012)
Michel Besserve, Dominik Janzing, Nikos K. Logothetis, Bernhard Schölkopf
Finding dependencies between frequencies with the kernel cross-spectral density. ICASSP (2011)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal Inference on Discrete Data Using Additive Noise Models. IEEE Trans. Pattern Anal. Mach. Intell. 33 (12) (2011)
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
Identifiability of Causal Graphs using Functional Models. UAI (2011)
Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
On Causal Discovery with Cyclic Additive Noise Models. NIPS (2011)
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI (2011)
Jakob Zscheischler, Dominik Janzing, Kun Zhang
Testing whether linear equations are causal: A free probability theory approach. UAI (2011)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang
Robust Learning via Cause-Effect Models CoRR (2011)
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
Detecting low-complexity unobserved causes. UAI (2011)

Dominik Janzing.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Dominik Janzing