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IFAU - Institute for Evaluation of Labour Market and Education Policy Working Paper Series
IFAU - Institute for Evaluation of Labour Market and Education Policy

No 2016:12:
Proxy variables and nonparametric identification of causal effects

Xavier de Luna, Philip Fowler () and Per Johansson

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.

Keywords: average treatment effect; observational studies; potential outcomes; unobserved confounders; (follow links to similar papers)

JEL-Codes: C14; (follow links to similar papers)

10 pages, June 30, 2016

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This paper is published as:
de Luna, Xavier, Philip Fowler and Per Johansson, (2017), 'Proxy variables and nonparametric identification of causal effects', Economics Letters, Vol. 150, January, No. January, pages 152-154

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