Proxy variables and nonparametric identification of causal effects
2017 (English)In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 150, 152-154 p.Article in journal (Refereed) Published
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.
Average treatment effect, Observational studies, Potential outcomes, Unobserved confounders
Economics and Business
IdentifiersURN: urn:nbn:se:uu:diva-316421DOI: 10.1016/j.econlet.2016.11.018ISI: 000392568300038OAI: oai:DiVA.org:uu-316421DiVA: diva2:1077940
FunderForte, Swedish Research Council for Health, Working Life and Welfare, DNR 2009-0826