Dominik Janzing

Affiliations:
  • Max Planck Institute for Intelligent Systems, Tübingen , Germany


According to our database1, Dominik Janzing authored at least 116 papers between 2001 and 2023.

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Bibliography

2023
Self-Compatibility: Evaluating Causal Discovery without Ground Truth.
CoRR, 2023

Toward Falsifying Causal Graphs Using a Permutation-Based Test.
CoRR, 2023

Reinterpreting causal discovery as the task of predicting unobserved joint statistics.
CoRR, 2023

Meaningful Causal Aggregation and Paradoxical Confounding.
CoRR, 2023

Bounding probabilities of causation through the causal marginal problem.
CoRR, 2023

Causal information splitting: Engineering proxy features for robustness to distribution shifts.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Assumption violations in causal discovery and the robustness of score matching.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Phenomenological Causality.
CoRR, 2022

Explaining the root causes of unit-level changes.
CoRR, 2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models.
CoRR, 2022

Correcting Confounding via Random Selection of Background Variables.
CoRR, 2022

Causal forecasting: generalization bounds for autoregressive models.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.
Proceedings of the International Conference on Machine Learning, 2022

On Measuring Causal Contributions via do-interventions.
Proceedings of the International Conference on Machine Learning, 2022

Causal Inference Through the Structural Causal Marginal Problem.
Proceedings of the International Conference on Machine Learning, 2022

Causal structure-based root cause analysis of outliers.
Proceedings of the International Conference on Machine Learning, 2022

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations.
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Obtaining Causal Information by Merging Datasets with MAXENT.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Causal Forecasting: Generalization Bounds for Autoregressive Models.
CoRR, 2021

Causal version of Principle of Insufficient Reason and MaxEnt.
CoRR, 2021

Necessary and sufficient conditions for causal feature selection in time series with latent common causes.
Proceedings of the 38th International Conference on Machine Learning, 2021

Why did the distribution change?
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

A Theory of Independent Mechanisms for Extrapolation in Generative Models.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Quantifying causal contribution via structure preserving interventions.
CoRR, 2020

Feature relevance quantification in explainable AI: A causal problem.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Analysis of cause-effect inference by comparing regression errors.
PeerJ Comput. Sci., 2019

Feature relevance quantification in explainable AI: A causality problem.
CoRR, 2019

Perceiving the arrow of time in autoregressive motion.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Selecting causal brain features with a single conditional independence test per feature.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Causal Regularization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions.
Proceedings of the Cause Effect Pairs in Machine Learning, 2019

2018
Does Universal Controllability of Physical Systems Prohibit Thermodynamic Cycles?
Open Syst. Inf. Dyn., 2018

Analysis of Cause-Effect Inference via Regression Errors.
CoRR, 2018

Detecting non-causal artifacts in multivariate linear regression models.
Proceedings of the 35th International Conference on Machine Learning, 2018

Cause-Effect Inference by Comparing Regression Errors.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Group invariance principles for causal generative models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Causal Consistency of Structural Equation Models.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Avoiding Discrimination through Causal Reasoning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Modeling confounding by half-sibling regression.
Proc. Natl. Acad. Sci. USA, 2016

Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach.
NeuroImage, 2016

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks.
J. Mach. Learn. Res., 2016

2015
Semi-supervised interpolation in an anticausal learning scenario.
J. Mach. Learn. Res., 2015

Telling cause from effect in deterministic linear dynamical systems.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Removing systematic errors for exoplanet search via latent causes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Inference of Cause and Effect with Unsupervised Inverse Regression.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Causal discovery with continuous additive noise models.
J. Mach. Learn. Res., 2014

Inferring causal structure: a quantum advantage.
CoRR, 2014

Estimating Causal Effects by Bounding Confounding.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Inferring latent structures via information inequalities.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Consistency of Causal Inference under the Additive Noise Model.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Replacing Causal Faithfulness with Algorithmic Independence of Conditionals.
Minds Mach., 2013

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

From Ordinary Differential Equations to Structural Causal Models: the deterministic case.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Causal Inference on Time Series using Restricted Structural Equation Models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Semi-supervised Learning in Causal and Anticausal Settings.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012
Causal Inference on Time Series using Structural Equation Models
CoRR, 2012

Information-geometric approach to inferring causal directions.
Artif. Intell., 2012

On causal and anticausal learning.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Causal Inference on Discrete Data Using Additive Noise Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Robust Learning via Cause-Effect Models
CoRR, 2011

Testing whether linear equations are causal: A free probability theory approach.
Proceedings of the UAI 2011, 2011

Kernel-based Conditional Independence Test and Application in Causal Discovery.
Proceedings of the UAI 2011, 2011

Identifiability of Causal Graphs using Functional Models.
Proceedings of the UAI 2011, 2011

Detecting low-complexity unobserved causes.
Proceedings of the UAI 2011, 2011

On Causal Discovery with Cyclic Additive Noise Models.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Finding dependencies between frequencies with the kernel cross-spectral density.
Proceedings of the IEEE International Conference on Acoustics, 2011

2010
Causal inference using the algorithmic Markov condition.
IEEE Trans. Inf. Theory, 2010

A promiseBQP-complete string rewriting problem.
Quantum Inf. Comput., 2010

Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory.
Open Syst. Inf. Dyn., 2010

Identifying Cause and Effect on Discrete Data using Additive Noise Models.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Distinguishing between cause and effect.
Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

Causality: Objectives and Assessment.
Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

Is there a physically universal cellular automaton or Hamiltonian?
CoRR, 2010

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery.
Proceedings of the UAI 2010, 2010

Inferring deterministic causal relations.
Proceedings of the UAI 2010, 2010

Probabilistic latent variable models for distinguishing between cause and effect.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Telling cause from effect based on high-dimensional observations.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Causal Markov Condition for Submodular Information Measures.
Proceedings of the COLT 2010, 2010

2009
Identifying confounders using additive noise models.
Proceedings of the UAI 2009, 2009

Detecting the direction of causal time series.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Regression by dependence minimization and its application to causal inference in additive noise models.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

09401 Abstracts Collection - Machine learning approaches to statistical dependences and causality.
Proceedings of the Machine learning approaches to statistical dependences and causality, 27.09., 2009

Quantum Entropy.
Proceedings of the Compendium of Quantum Physics, 2009

Entropy of Entanglement.
Proceedings of the Compendium of Quantum Physics, 2009

2008
Measuring 4-local qubit observables could probabilistically solve PSPACE.
Quantum Inf. Comput., 2008

Causal reasoning by evaluating the complexity of conditional densities with kernel methods.
Neurocomputing, 2008

How much is a quantum controller controlled by the controlled system?
Appl. Algebra Eng. Commun. Comput., 2008

Nonlinear causal discovery with additive noise models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Kernel Methods for Detecting the Direction of Time Series.
Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

2007
A Simple PromiseBQP-complete Matrix Problem.
Theory Comput., 2007

A kernel-based causal learning algorithm.
Proceedings of the Machine Learning, 2007

Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions.
Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Exploring the causal order of binary variables via exponential hierarchies of Markov kernels.
Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Learning causality by identifying common effects with kernel-based dependence measures.
Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

2006
Computer science approach to quantum control
PhD thesis, 2006

Guest editorial.
Inform. Forsch. Entwickl., 2006

Quantum computing models as a tool box for controlling and understanding the nanoscopic world.
Inform. Forsch. Entwickl., 2006

Causal Inference by Choosing Graphs with Most Plausible Markov Kernels.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2006

Computer science approach to quantum control.
Univeristätsverlag Karlsruhe, ISBN: 978-3-86644-083-8, 2006

2005
Ergodic Quantum Computing.
Quantum Inf. Process., 2005

Über den Zusammenhang zwischen thermodynamisch reversiblem, kryptograpisch seitenkanalfreiem sowie quantenkohärentem Rechnen.
Proceedings of the 35. Jahrestagung der Gesellschaft für Informatik, 2005

2003
Quasi-order of clocks and their synchronism and quantum bounds for copying timing information.
IEEE Trans. Inf. Theory, 2003

Treating the Independent Set Problem by 2D Ising Interactions with Adiabatic Quantum Computing.
Quantum Inf. Process., 2003

Two QCMA-complete problems.
Quantum Inf. Comput., 2003

On The Computational Power Of Physical Interactions: Bounds On The Number Of Time Steps For Simulating Arbitrary Interaction Graphs.
Int. J. Found. Comput. Sci., 2003

Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory
CoRR, 2003

2002
Universal simulation of Hamiltonians using a finite set of control operations.
Quantum Inf. Comput., 2002

Simulating arbitrary pair-interactions by a given Hamiltonian: graph-theoretical bounds on the time-complexity.
Quantum Inf. Comput., 2002

Quantum algorithm for measuring the eigenvalues of U ⊗ U<sup>-1</sup> for a black-box unitary transformation U.
Quantum Inf. Comput., 2002

Quantum algorithm for measuring the energy of n qubits with unknown pair-interactions.
Quantum Inf. Comput., 2002

Required sample size for learning sparse Bayesian networks with many variables
CoRR, 2002

2001
On using quantum protocols to detect traffic analysis.
Quantum Inf. Comput., 2001

Lower Bound on the Chromatic Number by Spectra of Weighted Adjacency Matrices
CoRR, 2001


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