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

Common causal estimands include the average treatment effect (ATE), theaverage treatment effect of the treated (ATT), and the average treatment effecton the controls (ATC). Using augmented inverse probability weighting methods,parametric models are judiciously leveraged to yield doubly robust estimators,i.e., estimators that are consistent when at least one the parametric models iscorrectly specified. Three sources of uncertainty are associated when weevaluate these estimators and their variances, i.e., when we estimate thetreatment and outcome regression models as well as the desired treatmenteffect.  In this paper, we propose methods to calculate the variance of thenormalized, doubly robust ATT and ATC estimators and investigate their finitesample properties. We consider both the asymptotic sandwich varianceestimation, the standard bootstrap as well as two wild bootstrap methods. Forthe asymptotic approximations, we incorporate the aforementioned uncertaintiesvia estimating equations. Moreover, unlike the standard bootstrap procedures,the proposed wild bootstrap methods use perturbations of the influencefunctions of the estimators through independently distributed random variables.We conduct an extensive simulation study where we vary the heterogeneity of thetreatment effect as well as the proportion of participants assigned to theactive treatment group. We illustrate the methods using an observational studyof critical ill patients on the use of right heart catherization.

Variance estimation for the average treatment effects on the treated and on the controls