Proposes nonparametric tests for treatment effect heterogeneity that avoid sample splitting, incorporate structured assumptions, and target policy-relevant alternatives comparing personalized vs. covariate-ignoring rules.
Cvxr: An r package for disciplined convex optimization
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EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.
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Nonparametric tests of treatment effect homogeneity for policy-makers
Proposes nonparametric tests for treatment effect heterogeneity that avoid sample splitting, incorporate structured assumptions, and target policy-relevant alternatives comparing personalized vs. covariate-ignoring rules.
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A Python Library For Empirical Calibration
EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.