期刊
BIOMETRIKA
卷 101, 期 2, 页码 423-437出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/ast066
关键词
Causal diagram; Confounder; Instrumental variable method; Proxy variable; Regression coefficient; Total effect
资金
- Ministry of Education, Culture, Sports, Science and Technology of Japan
- Asahi Glass Foundation
- Office of Naval Research
- National Institutes of Health
- National Science Foundation
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.
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