Proxy variables and nonparametric identification of causal effects

Author: Xavier de Luna, And Philip Fowler, And Per Johansson, And

Published in: Economics Letters, January 2017, vol. 150, pp. 152–154

Summary of 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.