Proxy variables and nonparametric identification of causal effects

Xavier de Luna, Philip Fowler and Per Johansson

Economics Letters, 2017, vol. 150, issue C, 152-154

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 outcomes 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)
Date: 2017
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Related works:
Working Paper: Proxy variables and nonparametric identification of causal effects (2016)
Working Paper: Proxy Variables and Nonparametric Identification of Causal Effects (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:150:y:2017:i:c:p:152-154

DOI: 10.1016/j.econlet.2016.11.018

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