A single scalar rank-stickiness parameter nonparametrically point-identifies the entire treatment-effect distribution by selecting the Bregman-Sinkhorn copula that maximizes average rank correlation subject to a relative-entropy constraint.
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Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.
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Nonparametric Point Identification of Treatment Effect Distributions via Rank Stickiness
A single scalar rank-stickiness parameter nonparametrically point-identifies the entire treatment-effect distribution by selecting the Bregman-Sinkhorn copula that maximizes average rank correlation subject to a relative-entropy constraint.
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Mind the Gap: Optimal and Equitable Encouragement Policies
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.