CORDIS Project
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The CAFES project aims to enhance causal inference methods in statistics and machine learning, particularly in cyclic systems. It focuses on developing algorithms that can handle feedback loops, with applications in various fields including biology and climate science.
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.…
UNIVERSITEIT VAN AMSTERDAM
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