Identification of Causal Effects Using Instrumental Variables

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Identification of Causal Effects Using Instrumental Variables

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dc.contributor.author Rubin, Donald B.
dc.contributor.author Imbens, Guido W
dc.contributor.author Angrist, Joshua D.
dc.date.accessioned 2009-11-05T18:01:38Z
dc.date.issued 1996
dc.identifier.citation Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. 1996. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91(434): 444-455. en_US
dc.identifier.issn 0162-1459 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:3382969
dc.description.abstract We outline a framework for causal inference in setting where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. To address the problems associated with comparing subjects by the ignorable assignment--an "intention-to-treat analysis"--we make use of instrumental variables, which have long been used by economists in the context of regression models with constant treatment effects. We show that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers. Without these assumptions, the IV estimand is simply the ratio of intention-to-treat causal estimands with no interpretation as an average causal effect. The advantages of embedding the IV approach in the RCM are that it clarifies the nature of critical assumptions needed for a causal interpretation, and moreover allows us to consider sensitivity of the results to deviations from key assumptions in a straightforward manner. We apply our analysis to estimate the effect of veteran status in the Vietnam era on mortality, using the lottery number assigned priority for the draft as an instrument, and we use our results to investigate the sensitivity of the conclusions to critical assumptions. en_US
dc.description.sponsorship Statistics en_US
dc.language.iso en_US en_US
dc.publisher American Statistical Association en_US
dc.relation.isversionof http://dx.doi.org/10.2307/2291629 en_US
dash.license META_ONLY
dc.subject intention-to-treat analysis en_US
dc.subject local average treatment effect en_US
dc.subject noncompliance en_US
dc.subject nonignorable treatment assignment en_US
dc.subject Rubin-Causal-Model en_US
dc.subject structural equation models en_US
dc.title Identification of Causal Effects Using Instrumental Variables en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Journal -- American Statistical Association en_US
dash.depositing.author Rubin, Donald B.
dash.embargo.until 10000-01-01

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  • FAS Scholarly Articles [6463]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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