World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
49
Citations
10426
World Ranking
4267
National Ranking
1221

Overview

Dominik Janzing is a researcher affiliated with Amazon in the United States, specializing in the field of computer science with a strong focus on artificial intelligence, statistics and probability, and several related subfields.

Their research primarily addresses topics such as Bayesian modeling and causal inference, advanced causal inference techniques, explainable artificial intelligence (XAI), statistical methods and inference, anomaly detection techniques and applications, generative adversarial networks and image synthesis, and fault detection and control systems.

Dominik Janzing has published extensively, contributing 58 research works mainly categorized under computer science. Within this broad domain, 46 publications relate to artificial intelligence, 15 to statistics and probability, 5 to computer vision and pattern recognition, and smaller numbers focusing on signal processing and control and systems engineering.

Their frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Causal Inference

Some noteworthy recent papers include:

  • DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models, 2022, arXiv (Cornell University)
  • You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction, 2021, arXiv (Cornell University)
  • A Theory of Independent Mechanisms for Extrapolation in Generative Models, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Necessary and sufficient conditions for causal feature selection in time series with latent common causes, 2020, arXiv (Cornell University)
  • Causal versions of maximum entropy and principle of insufficient reason, 2021, Journal of Causal Inference

Their collaborative network includes frequent co-authors such as Bernhard Schölkopf, Atalanti A. Mastakouri, Patrick Blöbaum, Francesco Locatello, and Michel Besserve.

Best Publications

  • Elements of Causal Inference: Foundations and Learning Algorithms

    Jonas Peters;Dominik Janzing;Bernhard Schölkopf

  • Nonlinear causal discovery with additive noise models

    Patrik O. Hoyer;Dominik Janzing;Joris M. Mooij;Jonas Peters

  • Elements of Causal Inference

    Jonas Peters;Dominik Janzing;Bernhard Schölkopf

  • Avoiding Discrimination through Causal Reasoning

    Niki Kilbertus;Mateo Rojas-Carulla;Giambattista Parascandolo;Moritz Hardt

  • Kernel-based conditional independence test and application in causal discovery

    Kun Zhang;Jonas Peters;Dominik Janzing;Bernhard Schölkopf

  • Causal discovery with continuous additive noise models

    Jonas Peters;Joris M. Mooij;Dominik Janzing;Bernhard Schölkopf

  • Distinguishing cause from effect using observational data: methods and benchmarks

    Joris M. Mooij;Jonas Peters;Dominik Janzing;Jakob Zscheischler

  • On causal and anticausal learning

    Dominik Janzing;Jonas Peters;Eleni Sgouritsa;Kun Zhang

  • Thermodynamic Cost of Reliability and Low Temperatures: Tightening Landauer's Principle and the Second Law

    Dominik Janzing;Pawel Wocjan;Robert Zeier;Rubino Geiss

  • Information-geometric approach to inferring causal directions

    Dominik Janzing;Joris Mooij;Kun Zhang;Jan Lemeire

  • Causal Inference Using the Algorithmic Markov Condition

    D Janzing;B Schölkopf

  • Quantifying causal influences

    Dominik Janzing;David Balduzzi;Moritz Grosse-Wentrup;Bernhard Schölkopf

  • On Causal and Anticausal Learning

    Bernhard Schoelkopf;Dominik Janzing;Jonas Peters;Eleni Sgouritsa

  • Causal Inference on Discrete Data Using Additive Noise Models

    J. Peters;D. Janzing;B. Scholkopf

  • Inferring deterministic causal relations

    Povilas Daniušis;Dominik Janzing;Joris Mooij;Jakob Zscheischler

  • Feature relevance quantification in explainable AI: A causal problem

    Dominik Janzing;Lenon Minorics;Patrick Blöbaum

  • A quantum advantage for inferring causal structure

    Katja Ried;Katja Ried;Megan Agnew;Lydia Vermeyden;Dominik Janzing

  • Regression by dependence minimization and its application to causal inference in additive noise models

    Joris Mooij;Dominik Janzing;Jonas Peters;Bernhard Schölkopf

  • Probabilistic latent variable models for distinguishing between cause and effect

    Oliver Stegle;Dominik Janzing;Kun Zhang;Joris M. Mooij

  • Identifiability of causal graphs using functional Models

    Jonas Peters;Joris M. Mooij;Dominik Janzing;Bernhard Schölkopf

  • Kernel-based Conditional Independence Test and Application in Causal Discovery

    Kun Zhang;Jonas Peters;Dominik Janzing;Bernhard Schoelkopf

  • Causal Inference on Time Series using Restricted Structural Equation Models

    Jonas Peters;Dominik Janzing;Bernhard Schölkopf

  • Causal inference using the algorithmic Markov condition

    Dominik Janzing;Bernhard Schoelkopf

  • The thermodynamic cost of reliability and low temperatures: Tightening Landauer's principle and the Second Law

    Dominik Janzing;Pawel Wocjan;Robert Zeier;Rubino Geiss

  • Quantifying causal influences

    Dominik Janzing;Moritz Grosse-Wentrup;Bernhard Scholkopf;David Balduzzi

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Jonas Peters
Jonas Peters ETH Zurich
Joris M. Mooij
Joris M. Mooij University of Amsterdam
Thomas Beth
Thomas Beth Karlsruhe University of Applied Sciences
Kun Zhang
Kun Zhang Carnegie Mellon University
Jakob Zscheischler
Jakob Zscheischler Helmholtz Centre for Environmental Research
Arthur Gretton
Arthur Gretton University College London
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
David W. Hogg
David W. Hogg Max Planck Society
Oliver Stegle
Oliver Stegle German Cancer Research Center

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