Fig. 3: From traditional statistical methods to machine learning in psychiatry.

This figure illustrates the shift from traditional statistical methods to advanced machine learning techniques in identifying different treatment benefits in psychiatry. Traditional statistical approaches typically yield results that reflect average treatment effects across a population, which may not accurately represent actual treatment effects in individual patients with heterogeneous characteristics. These methods, which test variations in treatment effects across individual patient characteristics using statistical significance cutoffs, are susceptible to false discoveries and false-negative results. In contrast, causal machine learning methods provide robust alternatives that are capable of more effectively identifying heterogeneous treatment effects. These methods offer a granular understanding of when treatments are beneficial or harmful, thereby enabling personalized decision-making in patient care that is tailored to individual patient profiles.