Proxy Variables and Nonparametric Identification of Causal Effects

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

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Year of publication:	2016
Authors:	de Luna, Xavier
Other Persons:	Fowler, Philip (contributor) ; Johansson, Per (contributor)
Publisher:	[2016]: [S.l.] : SSRN
Subject:	Nichtparametrisches Verfahren \| Nonparametric statistics \| Schätztheorie \| Estimation theory \| Kausalanalyse \| Causality analysis

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Extent:	1 Online-Ressource (10 p)
Series:	IZA Discussion Paper ; No. 10057
Type of publication:	Book / Working Paper
Language:	English
Other identifiers:	10.2139/ssrn.2810468 [DOI]
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012986751