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Fabrizia Mealli (University of Florence)

5 June 2012 @ 12:30

 

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Date:
5 June 2012
Time:
12:30
Event Category:

Using secondary outcomes and covariates to sharpen inference in randomized experiments with noncompliance

Restrictions implied by the randomization of treatment assignment on the joint distribution of a primary outcome and an auxiliary variable are used to tighten nonparametric bounds for intention-to-treat effects on the primary outcome for some latent subpopulations, without requiring the exclusion restriction assumption of the assignment. The auxiliary variable can be a secondary outcome or a covariate, while the subpopulations are defined by the values of the potential treatment status under each value of the assignment. The derived bounds can be used to detect violations of the exclusion restriction and the magnitude of these violations in instrumental variables settings. It is shown that the reduced width of the bounds depends on the the strength of the association of the auxiliary variable with the primary outcome and the compliance status. We also show how the setup we consider offers new identifying assumptions of intention-to-treat effects without the exclusion restriction. The use of the bounds is illustrated in two real data examples of a social job training experiment and a medical randomized encouragement study.