Causal inference in drug discovery and development
Quantitative Methods
2025-04-09 v1 Machine Learning
Applications
Abstract
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.
Cite
@article{arxiv.2209.14664,
title = {Causal inference in drug discovery and development},
author = {Tom Michoel and Jitao David Zhang},
journal= {arXiv preprint arXiv:2209.14664},
year = {2025}
}