Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/166013
Authors: 
DeLuna, Xavier
Fowler, Philip
Johansson, Per-Olov
Year of Publication: 
2016
Series/Report no.: 
Working Paper, IFAU - Institute for Evaluation of Labour Market and Education Policy 2016:12
Abstract: 
Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.
Subjects: 
average treatment effect
observational studies
potential outcomes
unobserved confounders
JEL: 
C14
Document Type: 
Working Paper

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