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Activity Number: 505 - Flexible Methods for Causality Research
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #328318 Presentation
Title: Sharp Instruments for Classifying Compliers and Generalizing Causal Effects
Author(s): Edward Kennedy*
Companies: Carnegie Mellon University
Keywords: causal inference; instrumental variable; noncompliance; local treatment effect

It is well-known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify "local" effects among compliers, i.e., those 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 is possible in some cases 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 their estimation error and asymptotic properties, showing that these tasks can in theory 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 bounds on identifiable subgroup effects. We develop an estimator of sharpness, and show that it is asymptotically efficient under weak conditions. We propose that sharpness be presented alongside strength to assess IV quality.

Authors who are presenting talks have a * after their name.

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