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

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 sho

SUBMITTER: Salditt M 

PROVIDER: S-EPMC11379759 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature