IJMPR Didactic Paper: Weighting for Causal Inference in Mental Health Research

被引：0

作者：

Cohn, Eric R. ^{[1
]}

Zubizarreta, Jose R. ^{[2
,3
,4
]}

机构：

[1] Westat Corp, New York, NY USA

[2] Harvard Med Sch, Dept Hlth Care Policy, Boston, MA 02115 USA

[3] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

[4] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA

来源：

INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH | 2025年 / 34卷 / 02期

基金：

巴西圣保罗研究基金会;

关键词：

causal inference; covariate balance; inverse probability weighting; observational studies; propensity scores; world mental health; PROPENSITY SCORE ESTIMATION; REGRESSION; VERSION;

D O I：

10.1002/mpr.70018

中图分类号：

R749 [精神病学];

学科分类号：

100205 ;

摘要：

Objective: Inverse probability weighting is a fundamental and general methodology for estimating the causal effects of exposures and interventions, but standard approaches to constructing such weights are often suboptimal. Methods: In this paper, we describe a recent approach for constructing such weights that directly balances covariates while optimizing the stability of the resulting weighting estimator. Results: To illustrate the use of this approach in mental health research, we present an exploratory study of the effects of exposure to violence on the risk of suicide attempt. Conclusions: The direct balancing approach to weighting should be given strong consideration in empirical research due to its robustness and transparency in building weighting estimators.

引用

页数：11

共 41 条

[1]

Ben-Michael E, 2021, Arxiv, DOI [arXiv:2110.14831, 10.48550/arXiv.2110.14831, DOI 10.48550/ARXIV.2110.14831]

[2] The epidemiology of traumatic event exposure worldwide: results from the World Mental Health Survey Consortium [J].