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UvA-DARE

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UvA-DARE

Results: 49

Number of items: 49

Blom, T., & Mooij, J. M. (2023). Causality and independence in perfectly adapted dynamical systems. Journal of Causal Inference, 11(1), Article 20210005. https://doi.org/10.1515/jci-2021-0005
Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
Blom, T., & Mooij, J. M. (2022). Robustness of model predictions under extension. Proceedings of Machine Learning Research, 180, 213-222. https://openreview.net/forum?id=BGGevIUicl9
Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [Thesis, fully internal, Universiteit van Amsterdam].
Versteeg, P., Zhang, C., & Mooij, J. M. (2022). Local Constraint-Based Causal Discovery under Selection Bias. Proceedings of Machine Learning Research, 177, 840-860. https://proceedings.mlr.press/v177/versteeg22a.html
Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786
de Kroon, A. A. W. M., Belgrave, D., & Mooij, J. M. (2022). Causal Bandits without prior knowledge using separating sets. Proceedings of Machine Learning Research, 177, 407-427. https://proceedings.mlr.press/v177/kroon22a.html
Mooij, J. M. (2022). Causality: from data to science.
Boeken, P. A., & Mooij, J. M. (2021). A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. Proceedings of Machine Learning Research, 161, 1565-1575. https://proceedings.mlr.press/v161/boeken21a.html
Blom, T. (2021). Causality and independence in systems of equations. [Thesis, fully internal, Universiteit van Amsterdam].

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