Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond

Functional unmasking (unblinding) in clinical trials for mental health treatments, especially with psychedelics, can bias results because participants often know they received the active drug due to its unmistakable acute effects. This undermines confidence that outcomes reflect true therapeutic properties rather than placebo-like effects. A counterfactual conceptualization formalizes the shortcomings of existing solutions like dose-response and active controls, and shows how modern causal inference approaches can isolate effects free of this contamination. Feedback mechanisms between perceived benefits and expectancies can make traditional methods obscure or exaggerate therapeutic benefits. The proposal motivates trial designs and statistical methods to mitigate the impacts of functional unmasking.

Study at a glance

Abstract

In clinical trials for mental health treatments, functional unmasking (unblinding) is a widespread challenge wherein participants become aware of their assigned treatment. Unmasking is especially concerning with psychedelics, due to the near unmistakable acute effects (the “trip”), resulting in uncertainty about whether outcomes following treatment reflect true therapeutic properties of the interventions, or placebo-like effects. We present a counterfactual conceptualization of unmasking that 1) formalizes the shortcomings of many existing statistical and experimental design solutions (e.g., dose-response, active controls), and 2) demonstrates how modern causal inference approaches can be applied to isolate effects devoid of this “contamination.” Our results reveal feedback mechanisms between perceived therapeutic benefits and expectancies that can render traditional methods prone to obscuring or exaggerating therapeutic benefits. Our proposal motivates trial designs and statistical methods that can be implemented to mitigate the impacts of functional unmasking.