bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function … for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal … bound on the bias depends only on the additional variation that the latent variables create both in the outcome and in the … (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias. Finally …

censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and …

bias arising from the presence of noise. Conditions are obtained under which this bias is asymptotically non … the density and quantile function. Our approach can be connected to corrections for selection bias and shrinkage …

We consider the bias of the 2SLS estimator in the linear instrumental vari-ables regression with one endogenous … regressor only. By using asymptotic expansion techniques we approximate 2SLS coefficient estimation bias under various scenarios … regarding the number and strength of instruments.The resulting approximation encompasses existing bias approximations, which are …