Stephan Bongers
Title
Cited by
Cited by
Year
Domain adaptation by using causal inference to predict invariant conditional distributions
S Magliacane, T van Ommen, T Claassen, S Bongers, P Versteeg, ...
Advances in Neural Information Processing Systems (NeurIPS 2018), 10846-10856, 2018
902018
Foundations of structural causal models with cycles and latent variables
S Bongers, P Forré, J Peters, JM Mooij
arXiv preprint arXiv:1611.06221, 2016
46*2016
Causal consistency of structural equation models
PK Rubenstein, S Weichwald, S Bongers, JM Mooij, D Janzing, ...
Proceedings of the 33rd Annual Conference on Uncertainty in Artificial …, 2017
352017
From random differential equations to structural causal models: The stochastic case
S Bongers, JM Mooij
arXiv preprint arXiv:1803.08784, 2018
272018
From deterministic ODEs to dynamic structural causal models
PK Rubenstein, S Bongers, B Schölkopf, JM Mooij
Proceedings of the 34th Annual Conference on Uncertainty in Artificial …, 2016
212016
Beyond structural causal models: Causal constraints models
T Blom, S Bongers, JM Mooij
Proceedings of the 35th Annual Conference on Uncertainty in Artificial …, 2020
19*2020
Geometric quantization of symplectic and Poisson manifolds
S Bongers
Utrecht University, 2014
122014
Causal transfer learning
S Magliacane, T van Ommen, T Claassen, S Bongers, P Versteeg, ...
arXiv preprint arXiv:1707.06422, 2017
92017
Bridging the Gap between Random Differential Equations and Structural Causal Models
S Bongers, JM Mooij
Causality Workshop at the 34th International Conference on Uncertainty in …, 2018
12018
Curing the Curse of Non-Recursiveness in Structural Causal Models
S Bongers, J Peters, B Schölkopf, JM Mooij
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Articles 1–10