Causal Analysis of Feedback Systems

Informazioni relative al progetto

CAFES

ID dell’accordo di sovvenzione: 639466

DOI 10.3030/639466

Progetto chiuso

Data della firma CE 8 Maggio 2015

Data di avvio 1 Settembre 2015

Data di completamento 31 Agosto 2020

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale € 1 405 652,00

Contributo UE € 1 405 652,00

Coordinato da

UNIVERSITEIT VAN AMSTERDAM
Netherlands

Pubblicazioni

Conference proceedings (10)

Algebraic Equivalence of Linear Structural Equation Models

Autori: Thijs van Ommen, Joris M. Mooij
Pubblicato in: Proceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence, Numero UAI 2017, 2017
Editore: Association for Uncertainty in Artificial Intelligence

Causal Effect Inference with Deep Latent-Variable Models

Autori: Louizos, Christos; Shalit, Uri; Mooij, Joris; Sontag, David; Zemel, Richard; Welling, Max
Pubblicato in: Advances in Neural Information Processing Systems 30, Numero NeurIPS 2017, 2017, Pagina/e 6446-6456
Editore: Curran Associates, Inc.

Causal Consistency of Structural Equation Models

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

From Deterministic ODEs to Dynamic Structural Causal Models

Autori: Rubenstein, Paul K.; Bongers, Stephan; Schoelkopf, Bernhard; Mooij, Joris M.
Pubblicato in: Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18), Numero UAI 2018, 2018
Editore: Association for Uncertainty in Artificial Intelligence

An Upper Bound for Random Measurement Error in Causal Discovery

Autori: Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
Pubblicato in: Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18), Numero UAI 2018, 2018
Editore: Association for Uncertainty in Artificial Intelligence

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

Autori: Patrick Forré, Joris M. Mooij
Pubblicato in: Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18), Numero UAI 2018, 2018
Editore: Association for Uncertainty in Artificial Intelligence

Beyond Structural Causal Models: Causal Constraints Models

Autori: Tineke Blom, Stephan Bongers, Joris M. Mooij
Pubblicato in: Proceedings of the 35th Annual Conference on Uncertainty in Artificial Intelligence, Numero UAI 2019, 2019
Editore: AUAI Press

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

Autori: Patrick Forré, Joris M. Mooij
Pubblicato in: Proceedings of the 35th Annual Conference on Uncertainty in Artificial Intelligence, Numero UAI 2019, 2019
Editore: AUAI Press

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

Autori: Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Pubblicato in: Advances in Neural Information Processing Systems (NeurIPS-2018), Numero NeurIPS 2018, 2018, Pagina/e 10869-10879
Editore: Curran Associates, Inc.

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

Autori: Mooij, Joris M.; Claassen, Tom
Pubblicato in: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), Numero 124, 2020, Pagina/e 1159-1168
Editore: Proceedings of Machine Learning Research