Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot

IF 1.8 4区数学 Q1 STATISTICS & PROBABILITY American Statistician Pub Date : 2024-01-10 DOI:10.1080/00031305.2024.2303419

Lauren D. Liao, Yeyi Zhu, Amanda L. Ngo, Rana F. Chehab, Samuel D. Pimentel

引用次数: 0

Abstract

Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline varia...

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用联合变量重要性图确定观察研究设计变量的优先次序

对治疗效果的观察研究需要对混杂变量进行调整。然而，因果推断方法通常无法对所有测量的基线变量进行完美的调整。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

American Statistician 数学-统计学与概率论

CiteScore

3.50

自引率

5.60%

发文量

审稿时长

>12 weeks

期刊介绍： Are you looking for general-interest articles about current national and international statistical problems and programs; interesting and fun articles of a general nature about statistics and its applications; or the teaching of statistics? Then you are looking for The American Statistician (TAS), published quarterly by the American Statistical Association. TAS contains timely articles organized into the following sections: Statistical Practice, General, Teacher''s Corner, History Corner, Interdisciplinary, Statistical Computing and Graphics, Reviews of Books and Teaching Materials, and Letters to the Editor.

期刊最新文献

Causal Inference with Complex Surveys: A Unified Perspective on Sample Selection and Exposure Selection Performance Analysis of NSUM Estimators in Social-Network Topologies Cross-validatory Z-Residual for Diagnosing Shared Frailty Models A Pareto tail plot without moment restrictions Sparse-group boosting: Unbiased group and variable selection