Kernel Methods for Detecting the Direction of Time Series. - Jonas Peters; Dominik Janzing; Arthur Gretton; Bernhard Schölkopf (GfKl, 2008)

Nonlinear causal discovery with additive noise models. - Patrik O. Hoyer; Dominik Janzing; Joris M. Mooij; Jonas Peters; Bernhard Schölkopf (NIPS, 2008)

Regression by dependence minimization and its application to causal inference in additive noise models. - Joris M. Mooij; Dominik Janzing; Jonas Peters; Bernhard Schölkopf (ICML, 2009)

Detecting the direction of causal time series. - Jonas Peters; Dominik Janzing; Arthur Gretton; Bernhard Schölkopf (ICML, 2009)

Identifying confounders using additive noise models. - Dominik Janzing; Jonas Peters; Joris M. Mooij; Bernhard Schölkopf (UAI, 2009)

Identifying Cause and Effect on Discrete Data using Additive Noise Models. - Jonas Peters; Dominik Janzing; Bernhard Schölkopf (AISTATS, 2010)

Detecting low-complexity unobserved causes. - Dominik Janzing; Eleni Sgouritsa; Oliver Stegle; Jonas Peters; Bernhard Schölkopf (UAI, 2011)

Identifiability of Causal Graphs using Functional Models. - Jonas Peters; Joris M. Mooij; Dominik Janzing; Bernhard Schölkopf (UAI, 2011)

Kernel-based Conditional Independence Test and Application in Causal Discovery. - Kun Zhang 001; Jonas Peters; Dominik Janzing; Bernhard Schölkopf (UAI, 2011)

Robust Learning via Cause-Effect Models - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Kun Zhang 001 (CoRR, 2011)

Causal Inference on Discrete Data Using Additive Noise Models. - Jonas Peters; Dominik Janzing; Bernhard Schölkopf (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

On causal and anticausal learning. - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Eleni Sgouritsa; Kun Zhang 001; Joris M. Mooij (ICML, 2012)

Causal Inference on Time Series using Structural Equation Models - Jonas Peters; Dominik Janzing; Bernhard Schölkopf (CoRR, 2012)

Counterfactual Reasoning and Learning Systems - Léon Bottou; Jonas Peters; Joaquin Quiñonero Candela; Denis Xavier Charles; Max Chickering; Elon Portugaly; Dipankar Ray; Patrice Y. Simard; Ed Snelson (CoRR, 2012)

Semi-supervised Learning in Causal and Anticausal Settings. - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Eleni Sgouritsa; Kun Zhang 001; Joris M. Mooij (Empirical Inference, 2013)

Causal Inference on Time Series using Restricted Structural Equation Models. - Jonas Peters; Dominik Janzing; Bernhard Schölkopf (NIPS, 2013)

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. - Eleni Sgouritsa; Dominik Janzing; Jonas Peters; Bernhard Schölkopf (UAI, 2013)

CAM: Causal Additive Models, high-dimensional order search and penalized regression. - Peter Bühlmann; Jonas Peters; Jan Ernest (CoRR, 2013)

Counterfactual reasoning and learning systems: the example of computational advertising. - Léon Bottou; Jonas Peters; Joaquin Quiñonero Candela; Denis Xavier Charles; Max Chickering; Elon Portugaly; Dipankar Ray; Patrice Y. Simard; Ed Snelson (J. Mach. Learn. Res., 2013)

Causal discovery with continuous additive noise models. - Jonas Peters; Joris M. Mooij; Dominik Janzing; Bernhard Schölkopf (J. Mach. Learn. Res., 2014)

Removing systematic errors for exoplanet search via latent causes. - Bernhard Schölkopf; David W. Hogg; Dun Wang; Daniel Foreman-Mackey; Dominik Janzing; Carl-Johann Simon-Gabriel; Jonas Peters (ICML, 2015)

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. - Dominik Rothenhäusler; Christina Heinze; Jonas Peters; Nicolai Meinshausen (NIPS, 2015)

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. - Bernhard Schölkopf; Krikamol Muandet; Kenji Fukumizu; Jonas Peters (CoRR, 2015)

Structural Intervention Distance for Evaluating Causal Graphs. - Jonas Peters; Peter Bühlmann (Neural Comput., 2015)

Computing functions of random variables via reproducing kernel Hilbert space representations. - Bernhard Schölkopf; Krikamol Muandet; Kenji Fukumizu; Stefan Harmeling; Jonas Peters (Stat. Comput., 2015)

The Arrow of Time in Multivariate Time Series. - Stefan Bauer; Bernhard Schölkopf; Jonas Peters (ICML, 2016)

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. - Stephan Bongers; Jonas Peters; Bernhard Schölkopf; Joris M. Mooij (CoRR, 2016)

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. - Joris M. Mooij; Jonas Peters; Dominik Janzing; Jakob Zscheischler; Bernhard Schölkopf (J. Mach. Learn. Res., 2016)

Modeling confounding by half-sibling regression. - Bernhard Schölkopf; David W. Hogg; Dun Wang; Daniel Foreman-Mackey; Dominik Janzing; Carl-Johann Simon-Gabriel; Jonas Peters (Proc. Natl. Acad. Sci. USA, 2016)

Identifying Causal Structure in Large-Scale Kinetic Systems. - Niklas Pfister; Stefan Bauer; Jonas Peters (CoRR, 2018)

Invariant Models for Causal Transfer Learning. - Mateo Rojas-Carulla; Bernhard Schölkopf; Richard E. Turner; Jonas Peters (J. Mach. Learn. Res., 2018)

Distributional Robustness of K-class Estimators and the PULSE. - Martin Emil Jakobsen; Jonas Peters (CoRR, 2020)

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables. - Rune Christiansen; Jonas Peters (J. Mach. Learn. Res., 2020)

Regularizing towards Causal Invariance: Linear Models with Proxies. - Michael Oberst; Nikolaj Thams; Jonas Peters; David A. Sontag (ICML, 2021)

Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. - Sebastian Weichwald; Søren Wengel Mogensen; Tabitha Edith Lee; Dominik Baumann; Oliver Kroemer; Isabelle Guyon; Sebastian Trimpe; Jonas Peters; Niklas Pfister (NeurIPS (Competition and Demos), 2021)

Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness. - Sebastian Weichwald; Jonas Peters (J. Cogn. Neurosci., 2021)

Causal Models for Dynamical Systems. - Jonas Peters; Stefan Bauer; Niklas Pfister (Probabilistic and Causal Inference, 2022)

Invariant Ancestry Search. - Phillip B. Mogensen; Nikolaj Thams; Jonas Peters (ICML, 2022)

Exploiting Independent Instruments: Identification and Distribution Generalization. - Sorawit Saengkyongam; Leonard Henckel; Niklas Pfister; Jonas Peters (ICML, 2022)

Identifiability of sparse causal effects using instrumental variables. - Niklas Pfister; Jonas Peters (UAI, 2022)

Structure Learning for Directed Trees. - Martin Emil Jakobsen; Rajen Dinesh Shah; Peter Bühlmann; Jonas Peters (J. Mach. Learn. Res., 2022)

A Causal Framework for Distribution Generalization. - Rune Christiansen; Niklas Pfister; Martin Emil Jakobsen; Nicola Gnecco; Jonas Peters (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Unfair Utilities and First Steps Towards Improving Them. - Frederik Hytting Jørgensen; Sebastian Weichwald; Jonas Peters (CoRR, 2023)

Boosted Control Functions. - Nicola Gnecco; Jonas Peters; Sebastian Engelke; Niklas Pfister (CoRR, 2023)

Invariant Policy Learning: A Causal Perspective. - Sorawit Saengkyongam; Nikolaj Thams; Jonas Peters; Niklas Pfister (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Identifying Representations for Intervention Extrapolation. - Sorawit Saengkyongam; Elan Rosenfeld; Pradeep Kumar Ravikumar; Niklas Pfister; Jonas Peters (ICLR, 2024)

Invariant Subspace Decomposition. - Margherita Lazzaretto; Jonas Peters; Niklas Pfister (CoRR, 2024)

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology. - Juan L. Gamella; Jonas Peters; Peter Bühlmann (CoRR, 2024)

DecoR: Deconfounding Time Series with Robust Regression. - Felix Schur; Jonas Peters (CoRR, 2024)

Effect-Invariant Mechanisms for Policy Generalization. - Sorawit Saengkyongam; Niklas Pfister; Predrag Klasnja; Susan A. Murphy; Jonas Peters (J. Mach. Learn. Res., 2024)

Causal chambers as a real-world physical testbed for AI methodology. - Juan L. Gamella; Jonas Peters; Peter Bühlmann (Nat. Mac. Intell., 2025)

Representation Learning for Distributional Perturbation Extrapolation. - Julius von Kügelgen; Jakob Ketterer; Xinwei Shen; Nicolai Meinshausen; Jonas Peters (CoRR, 2025)