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Proxy variables and nonparametric identification of causal effects
Umea Univ, Dept Stat, USBE, SE-90187 Umea, Sweden..
Umea Univ, Dept Stat, USBE, SE-90187 Umea, Sweden..
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics. Uppsala University, Units outside the University, Office of Labour Market Policy Evaluation. Inst Study Labor IZA, Bonn, Germany..
2017 (English)In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 150, 152-154 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 150, 152-154 p.
Keyword [en]
Average treatment effect, Observational studies, Potential outcomes, Unobserved confounders
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:uu:diva-316421DOI: 10.1016/j.econlet.2016.11.018ISI: 000392568300038OAI: oai:DiVA.org:uu-316421DiVA: diva2:1077940
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, DNR 2009-0826
Available from: 2017-03-01 Created: 2017-03-01 Last updated: 2017-03-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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