CORDIS Project
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CArLA focuses on creating a platform for transparent and explainable causal discovery in artificial intelligence. This project aims to improve understanding of causal relationships in data, particularly in high-stakes fields like healthcare and finance, allowing users to interact with and contest the findings.
Causal AI is widely perceived as crucial in the current AI landscape as it allows capturing causal effects amongst features in data, rather than simple correlations.
Causal discovery is an important aspect of machine learning as it paves the way towards achieving Causal AI.
It amounts to extracting causal graphs from data, encoding the structure of causal relations amongst features in data.
There is a gap in the state-of-the-art in AI as concerns causal discovery, in that most approaches are bla…
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