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Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions
Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij
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Causality and Independence in Perfectly Adapted Dynamical Systems
Tineke Blom and Joris M. Mooij
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Robustness of Model Predictions under Extension
Tineke Blom and Joris M. Mooij
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A Weaker Faithfulness Assumption based on Triple Interactions
Alexander Marx, Arthur Gretton, Joris M. Mooij
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Causal Discovery for Causal Bandits utilizing Separating Sets
Arnoud A. W. M. de Kroon, Danielle Belgrave, Joris M. Mooij
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A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery
Philip A. Boeken, Joris M. Mooij
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Conditional Independences and Causal Relations implied by Sets of Equations
Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij
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Foundations of Structural Causal Models with Cycles and Latent Variables
Stephan Bongers, Patrick Forré, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij
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From Random Differential Equations to Structural Causal Models: the stochastic case
Stephan Bongers, Joris M. Mooij
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Markov Properties for Graphical Models with Cycles and Latent Variables
Patrick Forré, Joris M. Mooij
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Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions
Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij
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Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip J. J. P. Versteeg, Joris M. Mooij
Advances in Neural Information Processing Systems 31 (
NeurIPS 2018)
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An Upper Bound for Random Measurement Error in Causal Discovery
Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (
UAI 2018)
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Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
Patrick Forré, Joris M. Mooij
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (
UAI 2018)
(The code published in the paper contains multiple bugs: one of the rules for sigma-separation is missing, and some of the conditioning rules were incorrectly implemented (the rules in Def. 2.18 and 2.19 in the paper should be correct). This has been fixed in the code in the github repository. The empirical results in the paper are hardly affected and the conclusions are unaltered.)
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From Deterministic ODEs to Dynamic Structural Causal Models
Paul K. Rubenstein, Stephan Bongers, Bernhard Schölkopf, Joris M. Mooij
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (
UAI 2018)