Causal inference in drug discovery and development

TL;DR

This paper introduces causal inference as a promising approach in drug discovery, highlighting its potential to improve decision-making and reduce bias, while discussing its applications, challenges, and opportunities in the field.

Contribution

It provides a non-technical overview of causal inference and reviews its recent applications in drug discovery and development.

Findings

Causal inference can enhance decision-making in drug discovery.

Application of causal methods is growing across the drug development pipeline.

Challenges include understanding and integrating causal concepts into practice.

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.