A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners.

Autor: Salditt M; Institut für Psychologie, University of Münster, Fliednerstr. 21, 48149, Münster, Germany. msalditt@uni-muenster.de., Eckes T; Institut für Psychologie, University of Münster, Fliednerstr. 21, 48149, Münster, Germany., Nestler S; Institut für Psychologie, University of Münster, Fliednerstr. 21, 48149, Münster, Germany.
Jazyk: angličtina
Zdroj: Administration and policy in mental health [Adm Policy Ment Health] 2024 Sep; Vol. 51 (5), pp. 650-673. Date of Electronic Publication: 2023 Nov 03.
DOI: 10.1007/s10488-023-01303-9
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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