Dominik Janzing

168 papers A* 24A 26B 3Misc 1Journal 90Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery. CoRR Ivaxi Sheth, Zhijing Jin, Bryan Wilder, Dominik Janzing, Mario Fritz
2025	J	`jnl`	Causal vs. Anticausal merging of predictors. CoRR Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf, Dominik Janzing
2025	—	`conf`	Cross-validating causal discovery via Leave-One-Variable-Out. CLeaR Daniela Schkoda, Philipp Michael Faller, Dominik Janzing, Patrick Blöbaum
2025	J	`jnl`	From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples? CoRR Sujai Hiremath, Dominik Janzing, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Kyra Gan
2025	J	`jnl`	On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models. CoRR Philipp Michael Faller, Dominik Janzing
2025	J	`jnl`	Root Cause Analysis of Outliers in Unknown Cyclic Graphs. CoRR Daniela Schkoda, Dominik Janzing
2025	A*	`conf`	Toward Falsifying Causal Graphs Using a Permutation-Based Test. AAAI Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing
2025	A	`conf`	Toward Universal Laws of Outlier Propagation. UAI Aram Ebtekar, Yuhao Wang, Dominik Janzing
2025	J	`jnl`	Toward Universal Laws of Outlier Propagation. CoRR Aram Ebtekar, Yuhao Wang, Dominik Janzing
2024	A*	`conf`	Causal vs. Anticausal merging of predictors. NeurIPS Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf, Dominik Janzing
2024	J	`jnl`	Cross-validating causal discovery via Leave-One-Variable-Out. CoRR Daniela Schkoda, Philipp Michael Faller, Patrick Blöbaum, Dominik Janzing
2024	J	`jnl`	DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models. J. Mach. Learn. Res. Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing
2024	—	`conf`	Meaningful Causal Aggregation and Paradoxical Confounding. CLeaR Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing
2024	J	`jnl`	Multiply-Robust Causal Change Attribution. CoRR Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman
2024	A	`conf`	Quantifying intrinsic causal contributions via structure preserving interventions. AISTATS Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki
2024	J	`jnl`	Root Cause Analysis of Outliers with Missing Structural Knowledge. CoRR Nastaran Okati, Sergio Hernan Garrido Mejia, William Roy Orchard, Patrick Blöbaum, Dominik Janzing
2024	A	`conf`	Self-Compatibility: Evaluating Causal Discovery without Ground Truth. AISTATS Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing
2023	A*	`conf`	Assumption violations in causal discovery and the robustness of score matching. NeurIPS Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
2023	J	`jnl`	Assumption violations in causal discovery and the robustness of score matching. CoRR Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
2023	J	`jnl`	Bounding probabilities of causation through the causal marginal problem. CoRR Numair Sani, Atalanti-Anastasia Mastakouri, Dominik Janzing
2023	J	`jnl`	Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts. CoRR Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Mila Hardt
2023	A	`conf`	Causal information splitting: Engineering proxy features for robustness to distribution shifts. UAI Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt
2023	—	`ed.`	Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. CLeaR Mihaela van der Schaar, Cheng Zhang, Dominik Janzing
2023	J	`jnl`	Meaningful Causal Aggregation and Paradoxical Confounding. CoRR Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing
2023	J	`jnl`	Reinterpreting causal discovery as the task of predicting unobserved joint statistics. CoRR Dominik Janzing, Philipp Michael Faller, Leena Chennuru Vankadara
2023	J	`jnl`	Self-Compatibility: Evaluating Causal Discovery without Ground Truth. CoRR Philipp Michael Faller, Leena Chennuru Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing
2023	J	`jnl`	Toward Falsifying Causal Graphs Using a Permutation-Based Test. CoRR Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing
2022	A*	`conf`	Causal Inference Through the Structural Causal Marginal Problem. ICML Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
2022	J	`jnl`	Causal Inference Through the Structural Causal Marginal Problem. CoRR Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
2022	A	`conf`	Causal forecasting: generalization bounds for autoregressive models. UAI Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
2022	A*	`conf`	Causal structure-based root cause analysis of outliers. ICML Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing
2022	—	`conf`	Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. CLeaR Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf
2022	J	`jnl`	Correcting Confounding via Random Selection of Background Variables. CoRR You-Lin Chen, Lenon Minorics, Dominik Janzing
2022	J	`jnl`	DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. CoRR Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing
2022	J	`jnl`	Explaining the root causes of unit-level changes. CoRR Kailash Budhathoki, George Michailidis, Dominik Janzing
2022	A	`conf`	Obtaining Causal Information by Merging Datasets with MAXENT. AISTATS Sergio Hernan Garrido Mejia, Elke Kirschbaum, Dominik Janzing
2022	A*	`conf`	On Measuring Causal Contributions via do-interventions. ICML Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
2022	J	`jnl`	Phenomenological Causality. CoRR Dominik Janzing, Sergio Hernan Garrido Mejia
2022	A*	`conf`	Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello
2022	J	`jnl`	Score matching enables causal discovery of nonlinear additive noise models. CoRR Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello
2022	A	`conf`	Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies. AISTATS Lenon Minorics, Ali Caner Türkmen, David Kernert, Patrick Blöbaum, Laurent Callot, Dominik Janzing
2022	A*	`conf`	You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf
2021	A*	`conf`	A Theory of Independent Mechanisms for Extrapolation in Generative Models. AAAI Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf
2021	J	`jnl`	Causal Forecasting: Generalization Bounds for Autoregressive Models. CoRR Leena C. Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
2021	J	`jnl`	Causal version of Principle of Insufficient Reason and MaxEnt. CoRR Dominik Janzing
2021	A*	`conf`	Necessary and sufficient conditions for causal feature selection in time series with latent common causes. ICML Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing
2021	A	`conf`	Why did the distribution change? AISTATS Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng
2021	J	`jnl`	Why did the distribution change? CoRR Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng
2021	J	`jnl`	You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf
2020	J	`jnl`	A theory of independent mechanisms for extrapolation in generative models. CoRR Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf
2020	A	`conf`	Feature relevance quantification in explainable AI: A causal problem. AISTATS Dominik Janzing, Lenon Minorics, Patrick Blöbaum
2020	J	`jnl`	Quantifying causal contribution via structure preserving interventions. CoRR Dominik Janzing, Patrick Blöbaum, Lenon Minorics
2019	J	`jnl`	Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
2019	A*	`conf`	Causal Regularization. NeurIPS Dominik Janzing
2019	J	`jnl`	Causal Regularization. CoRR Dominik Janzing
2019	J	`jnl`	Causal structure based root cause analysis of outliers. CoRR Dominik Janzing, Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum
2019	J	`jnl`	Feature relevance quantification in explainable AI: A causality problem. CoRR Dominik Janzing, Lenon Minorics, Patrick Blöbaum
2019	A*	`conf`	Perceiving the arrow of time in autoregressive motion. NeurIPS Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann
2019	A*	`conf`	Selecting causal brain features with a single conditional independence test per feature. NeurIPS Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing
2019	—	`ch.`	The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions. Cause Effect Pairs in Machine Learning Dominik Janzing
2018	J	`jnl`	Analysis of Cause-Effect Inference via Regression Errors. CoRR Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
2018	A	`conf`	Cause-Effect Inference by Comparing Regression Errors. AISTATS Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
2018	A*	`conf`	Detecting non-causal artifacts in multivariate linear regression models. ICML Dominik Janzing, Bernhard Schölkopf
2018	J	`jnl`	Detecting non-causal artifacts in multivariate linear regression models. CoRR Dominik Janzing, Bernhard Schölkopf
2018	J	`jnl`	Does Universal Controllability of Physical Systems Prohibit Thermodynamic Cycles? Open Syst. Inf. Dyn. Dominik Janzing, Pawel Wocjan
2018	A	`conf`	Group invariance principles for causal generative models. AISTATS Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing
2017	—	`conf`	Avoiding Discrimination through Causal Reasoning. NIPS Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
2017	J	`jnl`	Avoiding Discrimination through Causal Reasoning. CoRR Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
2017	A	`conf`	Causal Consistency of Structural Equation Models. UAI Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
2017	J	`jnl`	Causal Consistency of Structural Equation Models. CoRR Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
2017	J	`jnl`	Group invariance principles for causal generative models. CoRR Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing
2016	J	`jnl`	Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2016	J	`jnl`	Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach. NeuroImage Moritz Grosse-Wentrup, Dominik Janzing, Markus Siegel, Bernhard Schölkopf
2016	J	`jnl`	Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
2016	A	`ed.`	Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA UAI Alexander Ihler, Dominik Janzing
2015	A*	`conf`	Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, Dominik Janzing
2015	A	`conf`	Inference of Cause and Effect with Unsupervised Inverse Regression. AISTATS Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf
2015	A*	`conf`	Removing systematic errors for exoplanet search via latent causes. ICML Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
2015	J	`jnl`	Removing systematic errors for exoplanet search via latent causes. CoRR Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
2015	J	`jnl`	Semi-supervised interpolation in an anticausal learning scenario. J. Mach. Learn. Res. Dominik Janzing, Bernhard Schölkopf
2015	A*	`conf`	Telling cause from effect in deterministic linear dynamical systems. ICML Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve
2015	J	`jnl`	Telling cause from effect in deterministic linear dynamical systems. CoRR Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve
2014	J	`jnl`	Causal discovery with continuous additive noise models. J. Mach. Learn. Res. Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2014	A*	`conf`	Consistency of Causal Inference under the Additive Noise Model. ICML Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf
2014	J	`jnl`	Distinguishing cause from effect using observational data: methods and benchmarks. CoRR Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2014	A	`conf`	Estimating Causal Effects by Bounding Confounding. UAI Philipp Geiger, Dominik Janzing, Bernhard Schölkopf
2014	J	`jnl`	From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2014	J	`jnl`	Inferring causal structure: a quantum advantage. CoRR Katja Ried, Megan Agnew, Lydia Vermeyden, Dominik Janzing, Robert W. Spekkens, Kevin J. Resch
2014	A	`conf`	Inferring latent structures via information inequalities. UAI Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, Bernhard Schölkopf
2014	—	`ed.`	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. CI@UAI Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen
2013	—	`conf`	Causal Inference on Time Series using Restricted Structural Equation Models. NIPS Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2013	J	`jnl`	Consistency of Causal Inference under the Additive Noise Model. CoRR Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf
2013	J	`jnl`	From Ordinary Differential Equations to Structural Causal Models: the deterministic case CoRR Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2013	A	`conf`	From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2013	A	`conf`	Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2013	J	`jnl`	Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. CoRR Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2013	J	`jnl`	Replacing Causal Faithfulness with Algorithmic Independence of Conditionals. Minds Mach. Jan Lemeire, Dominik Janzing
2013	—	`conf`	Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
2012	J	`jnl`	Causal Inference on Time Series using Structural Equation Models CoRR Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2012	J	`jnl`	Detecting low-complexity unobserved causes CoRR Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
2012	J	`jnl`	Identifiability of Causal Graphs using Functional Models CoRR Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2012	J	`jnl`	Identifying confounders using additive noise models CoRR Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
2012	J	`jnl`	Inferring deterministic causal relations CoRR Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
2012	J	`jnl`	Information-geometric approach to inferring causal directions. Artif. Intell. Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf
2012	J	`jnl`	Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery CoRR Kun Zhang, Bernhard Schölkopf, Dominik Janzing
2012	J	`jnl`	Kernel-based Conditional Independence Test and Application in Causal Discovery CoRR Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2012	A*	`conf`	On causal and anticausal learning. ICML Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
2012	J	`jnl`	Testing whether linear equations are causal: A free probability theory approach CoRR Jakob Zscheischler, Dominik Janzing, Kun Zhang
2011	J	`jnl`	Causal Inference on Discrete Data Using Additive Noise Models. IEEE Trans. Pattern Anal. Mach. Intell. Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2011	A	`conf`	Detecting low-complexity unobserved causes. UAI Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
2011	Misc	`conf`	Finding dependencies between frequencies with the kernel cross-spectral density. ICASSP Michel Besserve, Dominik Janzing, Nikos K. Logothetis, Bernhard Schölkopf
2011	A	`conf`	Identifiability of Causal Graphs using Functional Models. UAI Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2011	A	`conf`	Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2011	—	`conf`	On Causal Discovery with Cyclic Additive Noise Models. NIPS Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
2011	J	`jnl`	Robust Learning via Cause-Effect Models CoRR Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang
2011	A	`conf`	Testing whether linear equations are causal: A free probability theory approach. UAI Jakob Zscheischler, Dominik Janzing, Kun Zhang
2010	J	`jnl`	A promiseBQP-complete string rewriting problem. Quantum Inf. Comput. Dominik Janzing, Pawel Wocjan
2010	A*	`conf`	Causal Markov Condition for Submodular Information Measures. COLT Bastian Steudel, Dominik Janzing, Bernhard Schölkopf
2010	J	`jnl`	Causal Markov condition for submodular information measures CoRR Bastian Steudel, Dominik Janzing, Bernhard Schölkopf
2010	J	`jnl`	Causal inference using the algorithmic Markov condition. IEEE Trans. Inf. Theory Dominik Janzing, Bernhard Schölkopf
2010	—	`ed.`	Causality: Objectives and Assessment (NIPS 2008 Workshop), Whistler, Canada, December 12, 2008 NIPS Causality: Objectives and Assessment Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf
2010	—	`conf`	Causality: Objectives and Assessment. NIPS Causality: Objectives and Assessment Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf
2010	—	`conf`	Distinguishing between cause and effect. NIPS Causality: Objectives and Assessment Joris M. Mooij, Dominik Janzing
2010	A	`conf`	Identifying Cause and Effect on Discrete Data using Additive Noise Models. AISTATS Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2010	A	`conf`	Inferring deterministic causal relations. UAI Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
2010	A	`conf`	Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI Kun Zhang, Bernhard Schölkopf, Dominik Janzing
2010	J	`jnl`	Is there a physically universal cellular automaton or Hamiltonian? CoRR Dominik Janzing
2010	J	`jnl`	Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory. Open Syst. Inf. Dyn. Dominik Janzing, Bastian Steudel
2010	—	`conf`	Probabilistic latent variable models for distinguishing between cause and effect. NIPS Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf
2010	A*	`conf`	Telling cause from effect based on high-dimensional observations. ICML Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf
2009	—	`conf`	09401 Abstracts Collection - Machine learning approaches to statistical dependences and causality. Machine learning approaches to statistical dependences and causality Dominik Janzing, Steffen L. Lauritzen, Bernhard Schölkopf
2009	A*	`conf`	Detecting the direction of causal time series. ICML Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
2009	—	`ch.`	Entropy of Entanglement. Compendium of Quantum Physics Dominik Janzing
2009	A	`conf`	Identifying confounders using additive noise models. UAI Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
2009	J	`jnl`	Justifying additive-noise-model based causal discovery via algorithmic information theory CoRR Dominik Janzing, Bastian Steudel
2009	—	`ed.`	Machine learning approaches to statistical dependences and causality, 27.09. - 02.10.2009 Machine learning approaches to statistical dependences and causality Dominik Janzing, Steffen L. Lauritzen, Bernhard Schölkopf
2009	—	`ch.`	Quantum Entropy. Compendium of Quantum Physics Dominik Janzing
2009	A*	`conf`	Regression by dependence minimization and its application to causal inference in additive noise models. ICML Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2008	J	`jnl`	Causal inference using the algorithmic Markov condition CoRR Dominik Janzing, Bernhard Schölkopf
2008	J	`jnl`	Causal reasoning by evaluating the complexity of conditional densities with kernel methods. Neurocomputing Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf
2008	J	`jnl`	How much is a quantum controller controlled by the controlled system? Appl. Algebra Eng. Commun. Comput. Dominik Janzing, Thomas Decker
2008	—	`conf`	Kernel Methods for Detecting the Direction of Time Series. GfKl Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
2008	J	`jnl`	Measuring 4-local qubit observables could probabilistically solve PSPACE. Quantum Inf. Comput. Pawel Wocjan, Dominik Janzing, Thomas Decker
2008	—	`conf`	Nonlinear causal discovery with additive noise models. NIPS Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf
2007	J	`jnl`	A Simple PromiseBQP-complete Matrix Problem. Theory Comput. Dominik Janzing, Pawel Wocjan
2007	A*	`conf`	A kernel-based causal learning algorithm. ICML Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu
2007	B	`conf`	Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions. ESANN Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf
2007	B	`conf`	Exploring the causal order of binary variables via exponential hierarchies of Markov kernels. ESANN Xiaohai Sun, Dominik Janzing
2007	B	`conf`	Learning causality by identifying common effects with kernel-based dependence measures. ESANN Xiaohai Sun, Dominik Janzing
2006	—	`conf`	Causal Inference by Choosing Graphs with Most Plausible Markov Kernels. AI&M Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf
2006	—	`—`	Computer science approach to quantum control Dominik Janzing
2006	—	`book`	Computer science approach to quantum control. Dominik Janzing
2006	J	`jnl`	Guest editorial. Inform. Forsch. Entwickl. Dominik Janzing, Jörn Müller-Quade
2006	J	`jnl`	Quantum computing models as a tool box for controlling and understanding the nanoscopic world. Inform. Forsch. Entwickl. Dominik Janzing
2005	J	`jnl`	Ergodic Quantum Computing. Quantum Inf. Process. Dominik Janzing, Pawel Wocjan
2005	—	`conf`	Über den Zusammenhang zwischen thermodynamisch reversiblem, kryptograpisch seitenkanalfreiem sowie quantenkohärentem Rechnen. GI Jahrestagung (1) Dominik Janzing
2003	J	`jnl`	On The Computational Power Of Physical Interactions: Bounds On The Number Of Time Steps For Simulating Arbitrary Interaction Graphs. Int. J. Found. Comput. Sci. Dominik Janzing, Pawel Wocjan, Thomas Beth
2003	J	`jnl`	Quasi-order of clocks and their synchronism and quantum bounds for copying timing information. IEEE Trans. Inf. Theory Dominik Janzing, Thomas Beth
2003	J	`jnl`	Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory CoRR Dominik Janzing, Daniel Herrmann
2003	J	`jnl`	Treating the Independent Set Problem by 2D Ising Interactions with Adiabatic Quantum Computing. Quantum Inf. Process. Pawel Wocjan, Dominik Janzing, Thomas Beth
2003	J	`jnl`	Two QCMA-complete problems. Quantum Inf. Comput. Pawel Wocjan, Dominik Janzing, Thomas Beth
2002	J	`jnl`	Quantum algorithm for measuring the eigenvalues of U ⊗ U Quantum Inf. Comput. Dominik Janzing, Thomas Beth
2002	J	`jnl`	Quantum algorithm for measuring the energy of n qubits with unknown pair-interactions. Quantum Inf. Comput. Dominik Janzing
2002	J	`jnl`	Required sample size for learning sparse Bayesian networks with many variables CoRR Pawel Wocjan, Dominik Janzing, Thomas Beth
2002	J	`jnl`	Simulating arbitrary pair-interactions by a given Hamiltonian: graph-theoretical bounds on the time-complexity. Quantum Inf. Comput. Pawel Wocjan, Dominik Janzing, Thomas Beth
2002	J	`jnl`	Universal simulation of Hamiltonians using a finite set of control operations. Quantum Inf. Comput. Pawel Wocjan, Martin Rötteler, Dominik Janzing, Thomas Beth
2001	J	`jnl`	Lower Bound on the Chromatic Number by Spectra of Weighted Adjacency Matrices CoRR Pawel Wocjan, Dominik Janzing, Thomas Beth
2001	J	`jnl`	On using quantum protocols to detect traffic analysis. Quantum Inf. Comput. Rainer Steinwandt, Dominik Janzing, Thomas Beth