Proxy variables and nonparametric identification of causal effects
(English)Manuscript (preprint) (Other academic)
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.
average treatment effect, observational studies, potential outcomes, unobserved confounders
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-125940OAI: oai:DiVA.org:umu-125940DiVA: diva2:973798