Measurement bias and effect restoration in causal inference

被引：117

作者：

Kuroki, Manabu ^{[1
]}

Pearl, Judea ^{[2
]}

机构：

[1] Inst Stat Math, Dept Data Sci, Tachikawa, Tokyo 1908562, Japan

[2] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA

来源：

BIOMETRIKA | 2014年 / 101卷 / 02期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

Causal diagram; Confounder; Instrumental variable method; Proxy variable; Regression coefficient; Total effect; INSTRUMENTAL VARIABLES; MODELS; MISCLASSIFICATION; ERRORS; BOUNDS;

D O I：

10.1093/biomet/ast066

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, it discusses the control of unmeasured confounders in parametric and nonparametric models and the computational problem of obtaining bias-free effect estimates in such models. We derive new conditions under which causal effects can be restored by observing proxy variables of unmeasured confounders with/without external studies.

引用

页码：423 / 437

页数：15

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