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ORCIDDominik Janzing
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- affiliation: Max Planck Institute for Intelligent Systems, Tübingen , Germany
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2020 – today
- 2024
[j30]Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing:
DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models. J. Mach. Learn. Res. 25: 147:1-147:7 (2024)- [c63]Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki:
Quantifying intrinsic causal contributions via structure preserving interventions. AISTATS 2024: 2188-2196 - [c62]Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. AISTATS 2024: 4132-4140 - [c61]Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing:
Meaningful Causal Aggregation and Paradoxical Confounding. CLeaR 2024: 1192-1217 - [i54]Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman:
Multiply-Robust Causal Change Attribution. CoRR abs/2404.08839 (2024) - [i53]Nastaran Okati, Sergio Hernan Garrido Mejia, William Roy Orchard, Patrick Blöbaum, Dominik Janzing:
Root Cause Analysis of Outliers with Missing Structural Knowledge. CoRR abs/2406.05014 (2024) - 2023
- [c60]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 - [c59]Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt:
Causal information splitting: Engineering proxy features for robustness to distribution shifts. UAI 2023: 1401-1411 - [e5]Mihaela van der Schaar, Cheng Zhang, Dominik Janzing:
Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. Proceedings of Machine Learning Research 213, PMLR 2023 [contents] - [i52]Numair Sani, Atalanti-Anastasia Mastakouri, Dominik Janzing:
Bounding probabilities of causation through the causal marginal problem. CoRR abs/2304.02023 (2023) - [i51]Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing:
Meaningful Causal Aggregation and Paradoxical Confounding. CoRR abs/2304.11625 (2023) - [i50]Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Mila Hardt:
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts. CoRR abs/2305.05832 (2023) - [i49]Dominik Janzing, Philipp Michael Faller, Leena Chennuru Vankadara:
Reinterpreting causal discovery as the task of predicting unobserved joint statistics. CoRR abs/2305.06894 (2023) - [i48]Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing:
Toward Falsifying Causal Graphs Using a Permutation-Based Test. CoRR abs/2305.09565 (2023) - [i47]Philipp Michael Faller, Leena Chennuru Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. CoRR abs/2307.09552 (2023) - [i46]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 abs/2310.13387 (2023) - 2022
- [c58]Sergio Hernan Garrido Mejia, Elke Kirschbaum, Dominik Janzing:
Obtaining Causal Information by Merging Datasets with MAXENT. AISTATS 2022: 581-603 - [c57]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: 10534-10554 - [c56]Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf:
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. CLeaR 2022: 110-143 - [c55]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 - [c54]Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing:
Causal structure-based root cause analysis of outliers. ICML 2022: 2357-2369 - [c53]Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. ICML 2022: 7793-7824 - [c52]Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim:
On Measuring Causal Contributions via do-interventions. ICML 2022: 10476-10501 - [c51]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: 18741-18753 - [c50]Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal forecasting: generalization bounds for autoregressive models. UAI 2022: 2002-2012 - [i45]Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. CoRR abs/2202.01300 (2022) - [i44]You-Lin Chen, Lenon Minorics, Dominik Janzing:
Correcting Confounding via Random Selection of Background Variables. CoRR abs/2202.02150 (2022) - [i43]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 abs/2203.04413 (2022) - [i42]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 abs/2206.06821 (2022) - [i41]Kailash Budhathoki, George Michailidis, Dominik Janzing:
Explaining the root causes of unit-level changes. CoRR abs/2206.12986 (2022) - [i40]Dominik Janzing, Sergio Hernan Garrido Mejia:
Phenomenological Causality. CoRR abs/2211.09024 (2022) - 2021
- [c49]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A Theory of Independent Mechanisms for Extrapolation in Generative Models. AAAI 2021: 6741-6749 - [c48]Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng:
Why did the distribution change? AISTATS 2021: 1666-1674 - [c47]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing:
Necessary and sufficient conditions for causal feature selection in time series with latent common causes. ICML 2021: 7502-7511 - [i39]Dominik Janzing:
Causal version of Principle of Insufficient Reason and MaxEnt. CoRR abs/2102.03906 (2021) - [i38]Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng:
Why did the distribution change? CoRR abs/2102.13384 (2021) - [i37]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 abs/2110.05304 (2021) - [i36]Leena C. Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal Forecasting: Generalization Bounds for Autoregressive Models. CoRR abs/2111.09831 (2021) - 2020
- [c46]Dominik Janzing, Lenon Minorics, Patrick Blöbaum:
Feature relevance quantification in explainable AI: A causal problem. AISTATS 2020: 2907-2916 - [i35]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A theory of independent mechanisms for extrapolation in generative models. CoRR abs/2004.00184 (2020) - [i34]Dominik Janzing, Patrick Blöbaum, Lenon Minorics:
Quantifying causal contribution via structure preserving interventions. CoRR abs/2007.00714 (2020)
2010 – 2019
- 2019
- [j29]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. 5: e169 (2019) - [c45]Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann:
Perceiving the arrow of time in autoregressive motion. NeurIPS 2019: 2303-2314 - [c44]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing:
Selecting causal brain features with a single conditional independence test per feature. NeurIPS 2019: 12532-12543 - [c43]Dominik Janzing:
Causal Regularization. NeurIPS 2019: 12683-12693 - [p1]Dominik Janzing:
The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions. Cause Effect Pairs in Machine Learning 2019: 3-26 - [i33]Dominik Janzing:
Causal Regularization. CoRR abs/1906.12179 (2019) - [i32]Dominik Janzing, Lenon Minorics, Patrick Blöbaum:
Feature relevance quantification in explainable AI: A causality problem. CoRR abs/1910.13413 (2019) - [i31]Dominik Janzing, Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum:
Causal structure based root cause analysis of outliers. CoRR abs/1912.02724 (2019) - 2018
- [j28]Dominik Janzing, Pawel Wocjan:
Does Universal Controllability of Physical Systems Prohibit Thermodynamic Cycles? Open Syst. Inf. Dyn. 25(3): 1850016:1-1850016:25 (2018) - [c42]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. AISTATS 2018: 557-565 - [c41]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Cause-Effect Inference by Comparing Regression Errors. AISTATS 2018: 900-909 - [c40]Dominik Janzing, Bernhard Schölkopf:
Detecting non-causal artifacts in multivariate linear regression models. ICML 2018: 2250-2258 - [i30]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Analysis of Cause-Effect Inference via Regression Errors. CoRR abs/1802.06698 (2018) - [i29]Dominik Janzing, Bernhard Schölkopf:
Detecting non-causal artifacts in multivariate linear regression models. CoRR abs/1803.00810 (2018) - 2017
- [c39]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. NIPS 2017: 656-666 - [c38]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 - [i28]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. CoRR abs/1705.02212 (2017) - [i27]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. CoRR abs/1706.02744 (2017) - [i26]Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf:
Causal Consistency of Structural Equation Models. CoRR abs/1707.00819 (2017) - 2016
- [j27]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: 32:1-32:102 (2016) - [j26]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: 825-833 (2016) - [j25]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): 7391-7398 (2016) - [e4]Alexander Ihler, Dominik Janzing:
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA. AUAI Press 2016, ISBN 978-0-9966431-1-5 [contents] - 2015
- [j24]Dominik Janzing, Bernhard Schölkopf:
Semi-supervised interpolation in an anticausal learning scenario. J. Mach. Learn. Res. 16: 1923-1948 (2015) - [c37]Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf:
Inference of Cause and Effect with Unsupervised Inverse Regression. AISTATS 2015 - [c36]Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. ICML 2015: 285-294 - [c35]Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, Dominik Janzing:
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015: 1917-1925 - [c34]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: 2218-2226 - [i25]Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. CoRR abs/1503.01299 (2015) - [i24]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 abs/1505.03036 (2015) - 2014
- [j23]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15(1): 2009-2053 (2014) - [c33]Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf:
Consistency of Causal Inference under the Additive Noise Model. ICML 2014: 478-486 - [c32]Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, Bernhard Schölkopf:
Inferring latent structures via information inequalities. UAI 2014: 112-121 - [c31]Philipp Geiger, Dominik Janzing, Bernhard Schölkopf:
Estimating Causal Effects by Bounding Confounding. UAI 2014: 240-249 - [e3]Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen:
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. CEUR Workshop Proceedings 1274, CEUR-WS.org 2014 [contents] - [i23]Katja Ried, Megan Agnew, Lydia Vermeyden, Dominik Janzing, Robert W. Spekkens, Kevin J. Resch:
Inferring causal structure: a quantum advantage. CoRR abs/1406.5036 (2014) - [i22]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR abs/1408.2063 (2014) - [i21]Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf:
Distinguishing cause from effect using observational data: methods and benchmarks. CoRR abs/1412.3773 (2014) - 2013
- [j22]Jan Lemeire, Dominik Janzing:
Replacing Causal Faithfulness with Algorithmic Independence of Conditionals. Minds Mach. 23(2): 227-249 (2013) - [c30]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference 2013: 129-141 - [c29]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Time Series using Restricted Structural Equation Models. NIPS 2013: 154-162 - [c28]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI 2013 - [c27]Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI 2013 - [i20]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR abs/1304.7920 (2013) - [i19]Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. CoRR abs/1309.6860 (2013) - [i18]Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf:
Consistency of Causal Inference under the Additive Noise Model. CoRR abs/1312.5770 (2013) - 2012
- [j21]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. 182-183: 1-31 (2012) - [c26]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
On causal and anticausal learning. ICML 2012 - [i17]Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf:
Detecting low-complexity unobserved causes. CoRR abs/1202.3737 (2012) - [i16]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Identifiability of Causal Graphs using Functional Models. CoRR abs/1202.3757 (2012) - [i15]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. CoRR abs/1202.3775 (2012) - [i14]Jakob Zscheischler, Dominik Janzing, Kun Zhang:
Testing whether linear equations are causal: A free probability theory approach. CoRR abs/1202.3779 (2012) - [i13]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. CoRR abs/1203.3475 (2012) - [i12]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. CoRR abs/1203.3534 (2012) - [i11]Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf:
Identifying confounders using additive noise models. CoRR abs/1205.2640 (2012) - [i10]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Time Series using Structural Equation Models. CoRR abs/1207.5136 (2012) - 2011
- [j20]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Discrete Data Using Additive Noise Models. IEEE Trans. Pattern Anal. Mach. Intell. 33(12): 2436-2450 (2011) - [c25]Michel Besserve, Dominik Janzing, Nikos K. Logothetis, Bernhard Schölkopf:
Finding dependencies between frequencies with the kernel cross-spectral density. ICASSP 2011: 2080-2083 - [c24]Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf:
On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647 - [c23]Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf:
Detecting low-complexity unobserved causes. UAI 2011: 383-391 - [c22]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Identifiability of Causal Graphs using Functional Models. UAI 2011: 589-598 - [c21]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011: 804-813 - [c20]Jakob Zscheischler, Dominik Janzing, Kun Zhang:
Testing whether linear equations are causal: A free probability theory approach. UAI 2011: 839-846 - [i9]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang:
Robust Learning via Cause-Effect Models. CoRR abs/1112.2738 (2011) - 2010
- [j19]Dominik Janzing, Bastian Steudel:
Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory. Open Syst. Inf. Dyn. 17(2): 189-212 (2010) - [j18]Dominik Janzing, Pawel Wocjan:
A promiseBQP-complete string rewriting problem. Quantum Inf. Comput. 10(3&4): 234-257 (2010) - [j17]Dominik Janzing, Bernhard Schölkopf:
Causal inference using the algorithmic Markov condition. IEEE Trans. Inf. Theory 56(10): 5168-5194 (2010) - [c19]Bastian Steudel, Dominik Janzing, Bernhard Schölkopf:
Causal Markov Condition for Submodular Information Measures. COLT 2010: 464-476 - [c18]Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf:
Telling cause from effect based on high-dimensional observations. ICML 2010: 479-486 - [c17]Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf:
Probabilistic latent variable models for distinguishing between cause and effect. NIPS 2010: 1687-1695 - [c16]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. UAI 2010: 143-150 - [c15]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010: 717-724 - [c14]Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf:
Causality: Objectives and Assessment. NIPS Causality: Objectives and Assessment 2010: 1-42 - [c13]Joris M. Mooij, Dominik Janzing:
Distinguishing between cause and effect. NIPS Causality: Objectives and Assessment 2010: 147-156 - [c12]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Identifying Cause and Effect on Discrete Data using Additive Noise Models. AISTATS 2010: 597-604 - [e2]Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf:
Causality: Objectives and Assessment (NIPS 2008 Workshop), Whistler, Canada, December 12, 2008. JMLR Proceedings 6, JMLR.org 2010 [contents] - [i8]Bastian Steudel, Dominik Janzing, Bernhard Schölkopf:
Causal Markov condition for submodular information measures. CoRR abs/1002.4020 (2010) - [i7]Dominik Janzing:
Is there a physically universal cellular automaton or Hamiltonian? CoRR abs/1009.1720 (2010)
2000 – 2009
- 2009
- [c11]Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Regression by dependence minimization and its application to causal inference in additive noise models. ICML 2009: 745-752 - [c10]Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
Detecting the direction of causal time series. ICML 2009: 801-808 - [c9]Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf:
Identifying confounders using additive noise models. UAI 2009: 249-257 - [e1]Dominik Janzing, Steffen L. Lauritzen, Bernhard Schölkopf:
Machine learning approaches to statistical dependences and causality, 27.09. - 02.10.2009. Dagstuhl Seminar Proceedings 09401, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2009 [contents] - [r2]Dominik Janzing:
Entropy of Entanglement. Compendium of Quantum Physics 2009: 205-209 - [r1]