Proxy variables and nonparametric identification of causal effects

Xavier de Luna, Philip Fowler, Per Johansson

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.

MoreLess

Year of publication:	June 30, 2016
Authors:	DeLuna, Xavier ; Fowler, Philip ; Johansson, Per-Olov
Publisher:	Uppsala : IFAU, Institute for Evaluation of Labour Market and Education Policy
Subject:	average treatment effect \| observational studies \| potential outcomes \| unobserved confounders \| Kausalanalyse \| Causality analysis \| Nichtparametrisches Verfahren \| Nonparametric statistics \| Schätztheorie \| Estimation theory

Extent:	1 Online-Ressource (circa 14 Seiten)
Series:	Working papers / Institute for Evaluation of Labour Market and Education Policy. - Uppsala : [Verlag nicht ermittelbar], ISSN 1651-1166, ZDB-ID 2732993-8. - Vol. 2016:12
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Arbeitspapier ; Working Paper ; Graue Literatur ; Non-commercial literature
Language:	English
Other identifiers:	hdl:10419/166013 [Handle]
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011542479