Precision psychiatry needs causal inference.

Abstract

Objective: Psychiatric research applies statistical methods that can be divided in two frameworks: causal inference and prediction. Recent proposals suggest a down-prioritisation of causal inference and argue that prediction paves the road to 'precision psychiatry' (i.e., individualised treatment). In this perspective, we critically appraise these proposals.

Methods: We outline strengths and weaknesses of causal inference and prediction frameworks and describe the link between clinical decision-making and counterfactual predictions (i.e., causality). We describe three key causal structures that, if not handled correctly, may cause erroneous interpretations, and three pitfalls in prediction research.

Results: Prediction and causal inference are both needed in psychiatric research and their relative importance is context-dependent. When individualised treatment decisions are needed, causal inference is necessary.

Conclusion: This perspective defends the importance of causal inference for precision psychiatry.