Proxy variables and nonparametric identification of causal effects

Författare: Xavier de Luna, Och Philip Fowler, Och Per Johansson, Och

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.