D. Janzing

Most-cited papers

Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, B. Schölkopf
20172,430 citations
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Kernel-based Conditional Independence Test and Application in Causal Discovery
Kun Zhang, J. Peters, D. Janzing, B. Schölkopf
2011Conference on Uncertainty in Artificial Intelligence764 citations
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On causal and anticausal learning
B. Schölkopf, D. Janzing, J. Peters, Eleni Sgouritsa +2 more
2012International Conference on Machine Learning703 citations
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Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt +2 more
2017Neural Information Processing Systems657 citations
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Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
J. Peters, J. Janzing, D. Zscheischler, J. Schölkopf +5 more
2014Journal of machine learning research464 citations
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Feature relevance quantification in explainable AI: A causality problem
D. Janzing, Lenon Minorics, Patrick Blöbaum
2019International Conference on Artificial Intelligence and Statistics421 citations
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Information-geometric approach to inferring causal directions
D. Janzing, J. Mooij, Kun Zhang, J. Lemeire +4 more
2012Artificial Intelligence324 citations Open access
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Quantifying causal influences
D. Janzing, David Balduzzi, M. Grosse-Wentrup, B. Schölkopf
2012241 citations Open access
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Causal Inference on Time Series using Restricted Structural Equation Models
J. Peters, D. Janzing, B. Schölkopf
2013Neural Information Processing Systems225 citations
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Inferring deterministic causal relations
P. Daniušis, D. Janzing, J. Mooij, J. Zscheischler +3 more
2010Conference on Uncertainty in Artificial Intelligence203 citations
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UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models
Paul K. Rubenstein, S. Weichwald, S. Bongers, J. Mooij +3 more
2017192 citations
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Identifiability of Causal Graphs using Functional Models
J. Peters, J. Mooij, D. Janzing, B. Schölkopf
2011Conference on Uncertainty in Artificial Intelligence179 citations
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Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
J. Mooij, J. Peters, D. Janzing, J. Zscheischler +1 more
2014Journal of machine learning research152 citations
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Score matching enables causal discovery of nonlinear additive noise models
Paul Rolland, V. Cevher, Matthäus Kleindessner, Chris Russell +3 more
2022International Conference on Machine Learning147 citations Open access
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A quantum advantage for inferring causal structure
K. Ried, M. Agnew, L. Vermeyden, D. Janzing +2 more
2015Nature Physics143 citations Open access
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Probabilistic latent variable models for distinguishing between cause and effect
O. Stegle, D. Janzing, Kun Zhang, J. Mooij +1 more
2010Neural Information Processing Systems143 citations
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From Ordinary Differential Equations to Structural Causal Models: the deterministic case
J. Mooij, D. Janzing, B. Schölkopf
2013Conference on Uncertainty in Artificial Intelligence123 citations
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DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, P. Götz, Kailash Budhathoki, Atalanti A. Mastakouri +1 more
2022Journal of machine learning research119 citations Open access
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Causal structure based root cause analysis of outliers
D. Janzing, Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum
2019arXiv.org114 citations
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On Causal Discovery with Cyclic Additive Noise Models
J. Mooij, D. Janzing, T. Heskes, B. Schölkopf
2011Neural Information Processing Systems104 citations
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Cause-Effect Inference by Comparing Regression Errors
Patrick Blöbaum, D. Janzing, T. Washio, Shohei Shimizu +1 more
2018International Conference on Artificial Intelligence and Statistics95 citations
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Identifying Cause and Effect on Discrete Data using Additive Noise Models
J. Peters, D. Janzing, B. Schölkopf
2010International Conference on Artificial Intelligence and Statistics94 citations
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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Philipp Geiger, Kun Zhang, B. Schölkopf, Ming-Ming Gong +1 more
2014International Conference on Machine Learning85 citations
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Inference of Cause and Effect with Unsupervised Inverse Regression
Eleni Sgouritsa, D. Janzing, Philipp Hennig, B. Schölkopf
2015International Conference on Artificial Intelligence and Statistics81 citations
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Modeling confounding by half-sibling regression
B. Schölkopf, D. Hogg, Dun Wang, D. Foreman-Mackey +3 more
2016Proceedings of the National Academy of Sciences of the United States of America77 citations Open access
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Why did the distribution change?
Kailash Budhathoki, D. Janzing, Patrick Blöbaum, Hoiyi Ng
2021International Conference on Artificial Intelligence and Statistics66 citations
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Inferring causal structure: a quantum advantage
K. Ried, M. Agnew, L. Vermeyden, D. Janzing +2 more
2014arXiv.org66 citations Open access
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Thermodynamic limits of dynamic cooling.
A. Allahverdyan, K. Hovhannisyan, D. Janzing, G. Mahler
2011Physical review. E, Statistical, nonlinear, and soft matter physics64 citations Open access
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Causality : Objectives and Assessment
D. Janzing, B. Schoelkopf
201059 citations
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Telling cause from effect in deterministic linear dynamical systems
Naji Shajarisales, D. Janzing, B. Schölkopf, M. Besserve
2015International Conference on Machine Learning53 citations
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Group invariance principles for causal generative models
M. Besserve, Naji Shajarisales, B. Schölkopf, D. Janzing
2017International Conference on Artificial Intelligence and Statistics50 citations
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Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach
M. Grosse-Wentrup, D. Janzing, M. Siegel, B. Schölkopf
2016NeuroImage47 citations
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Inferring latent structures via information inequalities
R. Chaves, L. Luft, T. O. Maciel, D. Gross +2 more
2014Conference on Uncertainty in Artificial Intelligence46 citations
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Consistency of Causal Inference under the Additive Noise Model
Samory Kpotufe, Eleni Sgouritsa, D. Janzing, B. Schölkopf
2013International Conference on Machine Learning45 citations
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Detecting Confounding in Multivariate Linear Models via Spectral Analysis
D. Janzing, B. Schölkopf
201744 citations Open access
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Testing whether linear equations are causal: A free probability theory approach
J. Zscheischler, D. Janzing, Kun Zhang
2011Conference on Uncertainty in Artificial Intelligence41 citations
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You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Gehler +3 more
2021International Conference on Learning Representations39 citations
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On Measuring Causal Contributions via do-interventions
Yonghan Jung, S. Kasiviswanathan, Jin Tian, D. Janzing +2 more
2022International Conference on Machine Learning38 citations
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Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference
D. Janzing, R. Chaves, B. Schölkopf
201538 citations Open access
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Detecting non-causal artifacts in multivariate linear regression models
D. Janzing, B. Schölkopf
2018International Conference on Machine Learning37 citations
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Causal Markov condition for submodular information measures
Bastian Steudel, D. Janzing, B. Schölkopf
2010Annual Conference Computational Learning Theory37 citations
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Justifying Information-Geometric Causal Inference
D. Janzing, Bastian Steudel, Naji Shajarisales, B. Schölkopf
201434 citations Open access
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Causal Inference Through the Structural Causal Marginal Problem
Luigi Gresele, Julius von Kügelgen, Jonas M. Kubler, Elke Kirschbaum +2 more
2022International Conference on Machine Learning32 citations
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Toward Falsifying Causal Graphs Using a Permutation-Based Test
Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum, M. Hardt +1 more
2023AAAI Conference on Artificial Intelligence30 citations Open access
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Analysis of cause-effect inference by comparing regression errors
Patrick Blöbaum, D. Janzing, T. Washio, Shohei Shimizu +1 more
2018PeerJ Computer Science30 citations Open access
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A theory of independent mechanisms for extrapolation in generative models
M. Besserve, Rémy Sun, D. Janzing, B. Schölkopf
2020AAAI Conference on Artificial Intelligence28 citations Open access
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Semi-supervised interpolation in an anticausal learning scenario
D. Janzing, B. Schölkopf
2015Journal of machine learning research27 citations
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Semi-supervised Learning in Causal and Anticausal Settings
B. Schölkopf, D. Janzing, J. Peters, Eleni Sgouritsa +2 more
2013Empirical Inference25 citations
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Detecting low-complexity unobserved causes
D. Janzing, Eleni Sgouritsa, O. Stegle, J. Peters +1 more
2011Conference on Uncertainty in Artificial Intelligence25 citations
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Quantifying intrinsic causal contributions via structure preserving interventions
D. Janzing, Patrick Blöbaum, Atalanti A. Mastakouri, P. M. Faller +2 more
2020International Conference on Artificial Intelligence and Statistics22 citations
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