situations, even when selection is only on observables. -- Treatment effects ; propensity score ; bias ; unconfoundedness …We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

This paper uses the control function to develop a framework for testing for selection bias. The idea behind our … magnitude of potential selection bias. Averaging this correction term with respect to appropriate weights yields the degree of … selection bias for alternative treatment effects of interest. One advantage of our framework is that it motivates when is …

We highlight the importance of randomisation bias, a situation where the process of participation in a social … part remains testable under the experiment even in the presence of randomisation bias, and offers a way to correct the non … detailed labour market histories can control for most selection bias in the evaluation of labour market interventions. Finally …

policy purposes. In this paper we set out a theoretical framework for the systematic consideration of "randomisation bias …", and provide what is to our knowledge the first empirical evidence on this form of bias in an actual social experiment, the …

level does not fully alleviate the bias. In fact, the magnitude and direction of the bias depend on the size of the …