Causal Analysis of Feedback Systems

Información del proyecto

CAFES

Identificador del acuerdo de subvención: 639466

DOI 10.3030/639466

Proyecto cerrado

Fecha de la firma de la CE 8 Mayo 2015

Fecha de inicio 1 Septiembre 2015

Fecha de finalización 31 Agosto 2020

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total € 1 405 652,00

Aportación de la UE € 1 405 652,00

Coordinado por

UNIVERSITEIT VAN AMSTERDAM
Netherlands

Publicaciones

Conference proceedings (10)

Algebraic Equivalence of Linear Structural Equation Models

Autores: Thijs van Ommen, Joris M. Mooij
Publicado en: Proceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence, Edición UAI 2017, 2017
Editor: Association for Uncertainty in Artificial Intelligence

Causal Effect Inference with Deep Latent-Variable Models

Autores: Louizos, Christos; Shalit, Uri; Mooij, Joris; Sontag, David; Zemel, Richard; Welling, Max
Publicado en: Advances in Neural Information Processing Systems 30, Edición NeurIPS 2017, 2017, Página(s) 6446-6456
Editor: Curran Associates, Inc.

Causal Consistency of Structural Equation Models

Autores: Rubenstein, Paul K.; Weichwald, Sebastian; Bongers, Stephan; Mooij, Joris M.; Janzing, Dominik; Grosse-Wentrup, Moritz; Schölkopf, Bernhard
Publicado en: Proceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence, Edición UAI 2017, 2017
Editor: Association for Uncertainty in Artificial Intelligence

From Deterministic ODEs to Dynamic Structural Causal Models

Autores: Rubenstein, Paul K.; Bongers, Stephan; Schoelkopf, Bernhard; Mooij, Joris M.
Publicado en: Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18), Edición UAI 2018, 2018
Editor: Association for Uncertainty in Artificial Intelligence

An Upper Bound for Random Measurement Error in Causal Discovery

Autores: Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
Publicado en: Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18), Edición UAI 2018, 2018
Editor: Association for Uncertainty in Artificial Intelligence

Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders

Autores: Patrick Forré, Joris M. Mooij
Publicado en: Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18), Edición UAI 2018, 2018
Editor: Association for Uncertainty in Artificial Intelligence

Beyond Structural Causal Models: Causal Constraints Models

Autores: Tineke Blom, Stephan Bongers, Joris M. Mooij
Publicado en: Proceedings of the 35th Annual Conference on Uncertainty in Artificial Intelligence, Edición UAI 2019, 2019
Editor: AUAI Press

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

Autores: Patrick Forré, Joris M. Mooij
Publicado en: Proceedings of the 35th Annual Conference on Uncertainty in Artificial Intelligence, Edición UAI 2019, 2019
Editor: AUAI Press

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

Autores: Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Publicado en: Advances in Neural Information Processing Systems (NeurIPS-2018), Edición NeurIPS 2018, 2018, Página(s) 10869-10879
Editor: Curran Associates, Inc.

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

Autores: Mooij, Joris M.; Claassen, Tom
Publicado en: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), Edición 124, 2020, Página(s) 1159-1168
Editor: Proceedings of Machine Learning Research