English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This paper proposes a novel framework of aggregated intersection ofregression functions, where the target parameter is obtained by averaging theminimum (or maximum) of a collection of regression functions over the covariatespace. Such quantities include the lower and upper bounds on distributionaleffects (Frechet-Hoeffding, Makarov) and the optimal welfare in the statisticaltreatment choice problem. The proposed estimator -- the envelope scoreestimator -- is shown to have an oracle property, where the oracle knows theidentity of the minimizer for each covariate value. I apply this result to thebounds in the Roy model and the Horowitz-Manski-Lee bounds with a discreteoutcome. The proposed approach performs well empirically on the data from theOregon Health Insurance Experiment.

Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters