Optimal restricted estimation for more efficient longitudinal causal inference

被引：0

作者：

Kennedy, Edward H. ^{[1
]}

Joffe, Marshall M. ^{[1
]}

Small, Dylan S. ^{[2
]}

机构：

[1] Univ Penn, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA

[2] Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2015年 / 97卷

关键词：

Doubly robust; Generalized method of moments; Marginal structural model; Semiparametric efficiency; Structural nested model; Time-varying confounding; GENERALIZED-METHOD; MOMENTS;

D O I：

10.1016/j.spl.2014.11.022

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Efficient semiparametric estimation of longitudinal causal effects is often analytically or computationally intractable. We propose a novel restricted estimation approach for increasing efficiency, which can be used with other techniques, is straightforward to implement, and requires no additional modeling assumptions. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：185 / 191

页数：7

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