Marie Davidian

TL;DR: The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based upon weighting observations by the inverse of estimated covariates.

TL;DR: The authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method.

TL;DR: This discussion aims to complement the presentation of the authors by elaborating on the view from the vantage point of semi-parametric theory, focusing on the assumptions embedded in the statistical models leading to different “types” of estimators rather than on the forms of the estimators themselves.

TL;DR: In this article, the variance function estimation in heteroscedastic regression models is studied in a unified way, focusing on common methods proposed in the statistical and other literature, to make both general observations and compare different estimation schemes.