Proxy variables and nonparametric identification of causal effects
Publicerad i: Economics Letters, January 2017, vol. 150, pp. 152–154
Sammanfattning av Working paper 2016:12
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.
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