ArticleAdministration and policy in mental health2024
A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners.
Article in Administration and policy in mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.Journal of affective disorders · 2026Trial
- Digital positive affect intervention (PAI) versus self-monitoring placebo in the treatment of anxiety and depression: a two-arm randomized controlled trial (RCT).BMC psychiatry · 2025Trial
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Leveraging machine learning to personalize depression treatment: A pre-registered study of 828 adults randomized to a digital single-session intervention or waitlist.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- Beyond Recanalization: Neurovascular Unit Protection and Precision Adjunctive Therapy for Ischemic Stroke in the Reperfusion Era.Biomolecules · 2026Review
- Machine learning for chemotherapy decision-making in breast cancer using large language model.Frontiers in digital health · 2026Article
- An overview of modern machine learning methods for effect measure modification analyses in high-dimensional settings.SSM - population health · 2025Article
- Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.BMC medical informatics and decision making · 2025Article
- Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.European journal of epidemiology · 2025Article
- Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects.Behavior research methods · 2024Article
- Causal effect of video gaming on mental well-being in Japan 2020-2022.Nature human behaviour · 2024Article
- Treatment Personalization and Precision Mental Health Care: Where are we and where do we want to go?Administration and policy in mental health · 2024Article
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3 authors.
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Abstract
Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which are flexible algorithms that can be used to estimate personalized treatment effects. More specifically, meta-learners decompose treatment effect estimation into multiple prediction tasks, each of which can be solved by any machine learning model. We begin by reviewing necessary assumptions for interpreting the estimated treatment effects as causal, and then give an overview over key concepts of machine learning. Throughout the article, we use an illustrative data example to show how the different meta-learners can be implemented in R. We also point out how current popular practices in psychotherapy research fit into the meta-learning framework. Finally, we show how heterogeneous treatment effects can be analyzed, and point out some challenges in the implementation of meta-learners.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.