This paper investigates the effect that covariate measurement error has on a conventional treatment effect analysis built on an unconfoundedness restriction that embodies conditional independence restrictions in which there is conditioning on error free covariates. The approach uses small...

This paper investigates the effect that covariate measurement error has on a conventional treatment effect analysis built on an unconfoundedness restriction that embodies conditional independence restrictions in which there is conditioning on error free covariates. The approach uses small...

When selection bias can purely be attributed to observables, several estimators have been discussed in the literature to estimate the average effect of a binary treatment or policy on a scalar outcome. Identification typically exploits the unconfoundedness of the treatment, which is verified if...

Single equation instrumental variable models for discrete outcomes are shown to be set not point identifying for the structural functions that deliver the values of the discrete outcome. Identified sets are derived for a general nonparametric model and sharp set identification is demonstrated....

This paper studies single equation models for binary outcomes incorporating instrumental variable restrictions. The models are incomplete in the sense that they place no restriction on the way in which values of endogenous variables are generated. The models are set, not point, identifying. The...

The paper studies the partial identifying power of structural single equation threshold crossing models for binary responses when explanatory variables may be endogenous. The paper derives the sharp identified set of threshold functions for the case in which explanatory variables are discrete...