bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function … mean and variance have been derived. Given a closed-form expression for the bias, bias-corrected estimator of the …-parametric and easy to implement. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to …

-response does not depend on the potentially missing variables' realization. However, assuming missing-at-random may introduce bias …

local and marginal treatment effects. First, we characterize the bias, due to the omitted variables U, of (nonparametric …) regression and instrumental variables estimands, thereby generalizing the classic linear regression omitted variable bias formula …