Open Access
August 2020 Sharp instruments for classifying compliers and generalizing causal effects
Edward H. Kennedy, Sivaraman Balakrishnan, Max G’Sell
Ann. Statist. 48(4): 2008-2030 (August 2020). DOI: 10.1214/19-AOS1874


It is well known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify “local” effects among compliers, that is, those subjects who take treatment only when encouraged by the IV. Local effects are controversial since they seem to only apply to an unidentified subgroup; this has led many to denounce these effects as having little policy relevance. However, we show that such pessimism is not always warranted: it can be possible to accurately predict who compliers are, and obtain tight bounds on more generalizable effects in identifiable subgroups. We propose methods for doing so and study estimation error and asymptotic properties, showing that these tasks can sometimes be accomplished even with very weak IVs. We go on to introduce a new measure of IV quality called “sharpness,” which reflects the variation in compliance explained by covariates, and captures how well one can identify compliers and obtain tight bounds on identifiable subgroup effects. We develop an estimator of sharpness and show that it is asymptotically efficient under weak conditions. Finally, we explore finite-sample properties via simulation, and apply the methods to study canvassing effects on voter turnout. We propose that sharpness should be presented alongside strength to assess IV quality.


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Edward H. Kennedy. Sivaraman Balakrishnan. Max G’Sell. "Sharp instruments for classifying compliers and generalizing causal effects." Ann. Statist. 48 (4) 2008 - 2030, August 2020.


Received: 1 January 2018; Revised: 1 May 2019; Published: August 2020
First available in Project Euclid: 14 August 2020

MathSciNet: MR4134784
Digital Object Identifier: 10.1214/19-AOS1874

Primary: 62G05 , 62H30

Keywords: Causal inference , generalizability , instrumental variable , noncompliance , observational study

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.48 • No. 4 • August 2020
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