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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Regression adjustments are often considered by investigators to improve theestimation efficiency of causal effect in randomized experiments when thereexists many pre-experiment covariates. In this paper, we provide conditionsthat guarantee the penalized regression including the Ridge, Elastic Net andAdapive Lasso adjusted causal effect estimators are asymptotic normal and weshow that their asymptotic variances are no greater than that of the simpledifference-in-means estimator, as long as the penalized estimators are riskconsistent. We also provide conservative estimators for the asymptotic variancewhich can be used to construct asymptotically conservative confidence intervalsfor the average causal effect (ACE). Our results are obtained under theNeyman-Rubin potential outcomes model of randomized experiment when the numberof covariates is large. Simulation study shows the advantages of the penalizedregression adjusted ACE estimators over the difference-in-means estimator.

Penalized regression adjusted causal effect estimates in high dimensional randomized experiments