From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect.

Abstract

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity-as text or images in electronic health records-calls for complex models, such as machine learning. Here, we review how machine learning can be used to inform decisions for individualized interventions, a causal question. Going from prediction to causal effects is challenging, as no individual is seen as both treated and not. We detail how some data can support some causal claims and how to build causal estimators with machine learning. Beyond variable selection to adjust for confounding bias, we cover the broader notions of study design that make or break causal inference. As the problems span across diverse scientific communities, we use didactic yet statistically precise formulations to bridge machine learning to epidemiology.