Causal inference in drug discovery and development
Copyright policy )Causal inference in drug discovery and development
To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision making in drug discovery. While it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a non-technical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.
- University of Bergen Norway
- Roche (Switzerland) Switzerland
- University of Basel Switzerland
FOS: Computer and information sciences, Computer Science - Machine Learning, 330, Quantitative Biology - Quantitative Methods, Statistics - Applications, 004, Machine Learning (cs.LG), Causality, Knowledge, Bias, FOS: Biological sciences, Drug Discovery, Humans, Applications (stat.AP), Quantitative Methods (q-bio.QM)
FOS: Computer and information sciences, Computer Science - Machine Learning, 330, Quantitative Biology - Quantitative Methods, Statistics - Applications, 004, Machine Learning (cs.LG), Causality, Knowledge, Bias, FOS: Biological sciences, Drug Discovery, Humans, Applications (stat.AP), Quantitative Methods (q-bio.QM)
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