Causal inference and counterfactual prediction in machine learning for actionable healthcare

Causal inference and counterfactual prediction in machine learning for actionable healthcare 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.

This paper Identifies the following three points: