Causal model

Computer science

Counterfactual thinking

Psychiatry

Machine learning

Medicine

Psychological intervention

Observational study

Risk analysis (engineering)

Artificial intelligence

Psychology

Causal inference

Data science

Counterfactual conditional

Social psychology

Pathology

Nature Machine Intelligence

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 f...

Causal inference and counterfactual prediction in machine learning for actionable healthcare