Proxy Variables and Nonparametric Identification of Causal Effects

Xavier de Luna (), Philip Fowler () and Per Johansson
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Xavier de Luna: Umeå University
Philip Fowler: Umeå University

No 10057, IZA Discussion Papers from Institute of Labor Economics (IZA)

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: potential outcomes; observational studies; average treatment effect; unobserved confounders (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2016-07
New Economics Papers: this item is included in nep-ecm and nep-ger
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Published - published in: Economics Letters, 2017, 150, 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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