← Authors

Joris M. Mooij

24 papers · 2007–2024 · 3 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (3) πŸƒ Academic Marathon (17) πŸ—ΊοΈ Taxonomy Completionist (26) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (96) πŸ“ˆ Trend Setter πŸ’Ž Century Club (24) πŸ”₯ Unstoppable (9) πŸš€ Conference Pioneer

Conferences

UAI (9) NIPS (8) JMLR (7)

Top co-authors

Dominik Janzing (5) Tom Claassen (5) Bernhard SchΓΆlkopf (5) Hilbert J. Kappen (3) Tineke Blom (3) Sara Magliacane (3) Jonas Peters (3) Stephan Bongers (2) Oliver Stegle (2) Thijs van Ommen (1)

Keywords

causal discovery (9) causal inference (6) graphical model (6) additive noise model (4) structural causal model (4) probabilistic inference (3) belief propagation (3) factor graph (3) causal ordering (2) approximate inference (2) causal identifiability (2) feedback loop (2) conditional independence (2) observational datum (2) dynamical system (2) additive noise (2) bayesian network (2) structural equation model (2) causal effect (2) selection bia (2)

Papers

Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence – Preface UAI 2024
Establishing Markov equivalence in cyclic directed graphs UAI 2023
Correcting for selection bias and missing response in regression using privileged information UAI 2023
Robustness of model predictions under extension UAI 2022
A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery UAI 2021
Conditional independences and causal relations implied by sets of equations JMLR 2021
A weaker faithfulness assumption based on triple interactions UAI 2021
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles UAI 2020
Joint Causal Inference from Multiple Contexts JMLR 2020
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias UAI 2019
Beyond Structural Causal Models: Causal Constraints Models UAI 2019
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions NIPS 2018
Causal Effect Inference with Deep Latent-Variable Models NIPS 2017
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks JMLR 2016
Ancestral Causal Inference NIPS 2016
Causal Discovery with Continuous Additive Noise Models JMLR 2014
On Causal Discovery with Cyclic Additive Noise Models NIPS 2011
Efficient inference in matrix-variate Gaussian models with \iid observation noise NIPS 2011
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models JMLR 2010
Probabilistic latent variable models for distinguishing between cause and effect NIPS 2010
Bounds on marginal probability distributions NIPS 2008
Nonlinear causal discovery with additive noise models NIPS 2008
Truncating the Loop Series Expansion for Belief Propagation JMLR 2007
Loop Corrections for Approximate Inference on Factor Graphs JMLR 2007