Abstract
AbstractPsychotherapy 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.
Funder
Westfälische Wilhelms-Universität Münster
Publisher
Springer Science and Business Media LLC
Subject
Psychiatry and Mental health,Public Health, Environmental and Occupational Health,Health Policy,Pshychiatric Mental Health
Reference78 articles.
1. Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353–7360.
2. Athey, S., Wager, S., Hadad, V., Klosin, S., Muhelbach, N., Nie, X., & Schaelling, M. (2020, May). Part I: HTE (binary treatment). Retrieved May 28, 2023, from https://gsbdbi.github.io/ml_tutorial/hte_tutorial/hte_tutorial.html.
3. Athey, S., Wager, S., & Tibshirani, J. (2019). Generalized random forests. Annals of Statistics, 47, 1148–1178. https://doi.org/10.1214/18-AOS1709
4. Baker, H. J., Lawrence, P. J., Karalus, J., Creswell, C., & Waite, P. (2021). The effectiveness of psychological therapies for anxiety disorders in adolescents: A meta-analysis. Clinical Child and Family Psychology Review, 24, 765–782. https://doi.org/10.1007/s10567-021-00364-2
5. Barber, J. P., & Muenz, L. R. (1996). The role of avoidance and obsessiveness in matching patients to cognitive and interpersonal psychotherapy: Empirical findings from the treatment for depression collaborative research program. Journal of consulting and clinical psychology, 64(5), 951.