Proxy Variables and Nonparametric Identification of Causal Effects

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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Year of publication:	2016
Authors:	de Luna, Xavier ; Fowler, Philip ; Johansson, Per
Publisher:	Bonn : Institute for the Study of Labor (IZA)
Subject:	average treatment effect \| observational studies \| potential outcomes \| unobserved confounders

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Series:	IZA Discussion Papers ; 10057
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	86313288X [GVK] hdl:10419/145191 [Handle] RePEc:iza:izadps:dp10057 [RePEc]
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011524988