Journal Article

Causal inference and counterfactual prediction in machine learning for actionable healthcare

He, Xing, Koopman, James S., Sperrin, Matt, Min, Jae S., Prosperi, Mattia, Rich, Shannan, Bian, Jiang, Guo, Yi, Buchan, Iain E., Wang, Mo

2020

Overview

Big data, high-performance computing, and (deep) machine learning are increasingly becoming key to precision medicine—from identifying disease risks and taking preventive measures, to making diagnoses and personalizing treatment for individuals. Precision medicine, however, is not only about predicting risks and outcomes, but also about weighing interventions. Interventional clinical predictive models require the correct specification of cause and effect, and the calculation of so-called counterfactuals, that is, alternative scenarios. In biomedical research, observational studies are commonly affected by confounding and selection bias. Without robust assumptions, often requiring a priori domain knowledge, causal inference is not feasible. Data-driven prediction models are often mistakenly used to draw causal effects, but neither their parameters nor their predictions necessarily have a causal interpretation. Therefore, the premise that data-driven prediction models lead to trustable decisions/interventions for precision medicine is questionable. When pursuing intervention modelling, the bio-health informatics community needs to employ causal approaches and learn causal structures. Here we discuss how target trials (algorithmic emulation of randomized studies), transportability (the licence to transfer causal effects from one population to another) and prediction invariance (where a true causal model is contained in the set of all prediction models whose accuracy does not vary across different settings) are linchpins to developing and testing intervention models. Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/114/1305 / 692/308/409 / 706/648/160 / 706/648/496 / Automation / Bias / Big Data / Cancer therapies / Causality / Deep learning / Electronic health records / Engineering / Health care / Hypertension / Inference / Intervention / Lung cancer / Machine learning / Medical records / Model accuracy / Perspective / Precision medicine / Prediction models / Probability / Risk factors / Smoking / Variables