Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond

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.

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Publication Details

Journal
medRxiv
Published
2025-12-08
DOI
https://doi.org/10.64898/2025.12.05.25341713
Primary Topic
Psychedelics and Drug Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond

Alexander W. Levis, David B. Yaden, Sandeep M. Nayak, Gabriel Loewinger et al.
medRxiv
Psychedelics and Drug Studies
article

Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond

Alexander W. Levis, David B. Yaden, Sandeep M. Nayak, Gabriel Loewinger, Mats Julius Stensrud
article en

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.

medRxiv
Johns Hopkins University (US), Institute of Mathematics and Informatics (BG), Czech Academy of Sciences, Institute of Mathematics (CZ), Institute for Medical Informatics and Biostatistics (CH), École Polytechnique Fédérale de Lausanne (CH)
U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Mental Health
Openalex Percentile: Top 47%
Psychedelics and Drug Studies
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