Proxy variables and nonparametric identification of causal effects

Xavier de Luna, Philip Fowler () and Per Johansson
Additional contact information
Xavier de Luna: Department of statistics, USBE, Umeå University, Postal: Umeå, Sweden
Philip Fowler: Department of statistics, USBE, Umeå University, Postal: Umeå, Sweden

No 2016:12, Working Paper Series from IFAU - Institute for Evaluation of Labour Market and Education Policy

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 (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2016-06-30
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Published as de Luna, Xavier, Philip Fowler and Per Johansson, 'Proxy variables and nonparametric identification of causal effects' in Economics Letters, 2017, pages 152-154.

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Related works:
Journal Article: Proxy variables and nonparametric identification of causal effects (2017)
Working Paper: Proxy Variables and Nonparametric Identification of Causal Effects (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:ifauwp:2016_012

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