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CAFES SIGNED

Causal Analysis of Feedback Systems

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Project "CAFES" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITEIT VAN AMSTERDAM 

Organization address
address: SPUI 21
city: AMSTERDAM
postcode: 1012WX
website: www.uva.nl

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Total cost 1˙405˙652 €
 EC max contribution 1˙405˙652 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2014-STG
 Funding Scheme ERC-STG
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2020-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITEIT VAN AMSTERDAM NL (AMSTERDAM) coordinator 1˙405˙652.00

Map

 Project objective

Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making.

The key challenge that will be addressed is how to deal with cyclic causal relationships ('feedback loops'). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.

 Publications

year authors and title journal last update
List of publications.
2017 Thijs van Ommen, Joris M. Mooij
Algebraic Equivalence of Linear Structural Equation Models
published pages: , ISSN: , DOI:
Proceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence 33 2019-05-29
2017 Rubenstein, Paul K.; Weichwald, Sebastian; Bongers, Stephan; Mooij, Joris M.; Janzing, Dominik; Grosse-Wentrup, Moritz; Schölkopf, Bernhard
Causal Consistency of Structural Equation Models
published pages: , ISSN: , DOI:
Proceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence 33 2019-05-29
2017 Louizos, Christos; Shalit, Uri; Mooij, Joris; Sontag, David; Zemel, Richard; Welling, Max
Causal Effect Inference with Deep Latent-Variable Models
published pages: 6446-6456, ISSN: , DOI:
Advances in Neural Information Processing Systems 30 2019-05-29
2018 Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
An Upper Bound for Random Measurement Error in Causal Discovery
published pages: , ISSN: , DOI:
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18) 34 2019-04-18
2018 Patrick Forré, Joris M. Mooij
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
published pages: , ISSN: , DOI:
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18) 34 2019-04-18
2018 Rubenstein, Paul K.; Bongers, Stephan; Schoelkopf, Bernhard; Mooij, Joris M.
From Deterministic ODEs to Dynamic Structural Causal Models
published pages: , ISSN: , DOI:
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-18) 34 2019-04-18

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The information about "CAFES" are provided by the European Opendata Portal: CORDIS opendata.

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