Heterogeneous treatment effects analysis for social scientists: A review.
Soc Sci Res
; 109: 102810, 2023 01.
Article
em En
| MEDLINE
| ID: mdl-36470639
Social scientists have long been interested in the varying responses to a specific intervention, motivating the enterprise of heterogeneous treatment effects (HTE) analysis. Over the past five decades, the rapid development of HTE methods, from conventional multiplicative interactions in linear models to explorations based on machine learning techniques, has been witnessed. This article presents a systematic review of major HTE methods, including multiplicative interaction modeling, generalized additive modeling, propensity-score-based methods, marginal treatment effect, separate LASSO constraints, causal trees, causal forests, Bayesian additive regression trees, and meta-learners (i.e., the S-learner, T-learner, X-learner, and R-learner). These methods, as described roughly in a chronological order to emphasize methodological developments, are addressed to highlight their respective strengths and limitations. Following an illustrative example, this article reflects on future methodological developments.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
Soc Sci Res
Ano de publicação:
2023
Tipo de documento:
Article