Measurement bias and effect restoration in causal inference

• 成果类型: Article
• 署名作者: Kuroki, Manabu; Pearl, Judea
• 署名单位: Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of California System; University of California Los Angeles
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ast066
• 发表日期: 2014
• 页码: 423437
• 关键词: instrumental variables; models; misclassification; Identifiability; errors; bounds

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.