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

Treatment effect estimation under unconfoundedness is a fundamental task incausal inference. In response to the challenge of analyzing high-dimensionaldatasets collected in substantive fields such as epidemiology, genetics,economics, and social sciences, many methods for treatment effect estimationwith high-dimensional nuisance parameters (the outcome regression and thepropensity score) have been developed in recent years. However, it is stillunclear what is the necessary and sufficient sparsity condition on the nuisanceparameters for the treatment effect to be $\sqrt{n}$-estimable. In this paper,we propose a new Double-Calibration strategy that corrects the estimation biasof the nuisance parameter estimates computed by regularized high-dimensionaltechniques and demonstrate that the corresponding Doubly-Calibrated estimatorachieves $1 / \sqrt{n}$-rate as long as one of the nuisance parameters issparse with sparsity below $\sqrt{n} / \log p$, where $p$ denotes the ambientdimension of the covariates, whereas the other nuisance parameter can bearbitrarily complex and completely misspecified. The Double-Calibrationstrategy can also be applied to settings other than treatment effectestimation, e.g. regression coefficient estimation in the presence of divergingnumber of controls in a semiparametric partially linear model.

Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity