World's Best Scientists 2026 revealed!

Overview

Joris M. Mooij is affiliated with the University of Amsterdam in the Netherlands. Their research primarily falls within the field of Computer Science with a focus on several subfields including Artificial Intelligence, Statistics and Probability, Molecular Biology, Management Science and Operations Research, and Computational Theory and Mathematics.

The scientist's main topics of work span a number of areas related to causal inference and computational methods. These include:

  • Bayesian Modeling and Causal Inference
  • Advanced Graph Neural Networks
  • Gene Regulatory Network Analysis
  • Logic, Reasoning, and Knowledge
  • Data Quality and Management
  • Statistical Methods and Inference
  • Protein Structure and Dynamics

Mooij has contributed extensively to the academic community with numerous publications. Key recent papers by the scientist include:

  • Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles, 2020, arXiv (Cornell University)
  • Causal Bandits without prior knowledge using separating sets, 2020, arXiv (Cornell University)
  • Local Constraint-Based Causal Discovery under Selection Bias, 2022, arXiv (Cornell University)
  • A Weaker Faithfulness Assumption based on Triple Interactions, 2020, arXiv (Cornell University)

Mooij's work appears frequently in the venue arXiv (Cornell University), with 21 publications, and also in the Journal of Causal Inference. Their collaborative network includes several frequent co-authors such as Philip Boeken, Tineke Blom, Onno Zoeter, Tom Claassen, and Patrick Forré.

Best Publications

  • MAGMA: Generalized Gene-Set Analysis of GWAS Data

    Christiaan A. de Leeuw;Joris M. Mooij;Tom Heskes;Danielle Posthuma

  • Nonlinear causal discovery with additive noise models

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

  • Causal discovery with continuous additive noise models

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

  • Causal Effect Inference with Deep Latent-Variable Models

    Christos Louizos;Uri Shalit;Joris M. Mooij;David A. Sontag

  • 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

  • libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

    Joris M. Mooij

  • Information-geometric approach to inferring causal directions

    Dominik Janzing;Joris Mooij;Kun Zhang;Jan Lemeire

  • Sufficient Conditions for Convergence of the Sum–Product Algorithm

    J.M. Mooij;H.J. Kappen

  • On Causal and Anticausal Learning

    Bernhard Schoelkopf;Dominik Janzing;Jonas Peters;Eleni Sgouritsa

  • Inferring deterministic causal relations

    Povilas Daniušis;Dominik Janzing;Joris Mooij;Jakob Zscheischler

  • Methods for causal inference from gene perturbation experiments and validation.

    Nicolai Meinshausen;Alain Hauser;Joris M. Mooij;Jonas Peters

  • 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

  • Remote Sensing Feature Selection by Kernel Dependence Measures

    G Camps-Valls;J Mooij;B Scholkopf

  • Efficient inference in matrix-variate Gaussian models with iid observation noise

    Oliver Stegle;Christoph Lippert;Joris M. Mooij;Neil D. Lawrence

  • Learning sparse causal models is not NP-hard

    Tom Claassen;Joris M. Mooij;Tom Heskes

  • On Causal Discovery with Cyclic Additive Noise Models

    Joris M. Mooij;Dominik Janzing;Tom Heskes;Bernhard Schölkopf

  • From ordinary differential equations to structural causal models: the deterministic case

    Joris M. Mooij;Dominik Janzing;Bernhard Schölkopf

Frequent Co-Authors

Dominik Janzing
Dominik Janzing Amazon (United States)
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Hilbert J. Kappen
Hilbert J. Kappen Radboud University
Tom Heskes
Tom Heskes Radboud University
Kun Zhang
Kun Zhang Carnegie Mellon University
Jakob Zscheischler
Jakob Zscheischler Helmholtz Centre for Environmental Research
Max Welling
Max Welling University of Amsterdam
Oliver Stegle
Oliver Stegle German Cancer Research Center
Arthur Gretton
Arthur Gretton University College London

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