Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/145191
Authors: 
de Luna, Xavier
Fowler, Philip
Johansson, Per
Year of Publication: 
2016
Series/Report no.: 
IZA Discussion Papers 10057
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

Files in This Item:
File
Size
236.7 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.