Correcting Attrition Bias via Changes-in-Changes
Abstract
Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method can be applied to randomized experiments as well as difference-in-difference designs. A simulation experiment points to the advantages of this approach relative to one of the most commonly used approaches in the literature, inverse probability weighting. Those advantages are further illustrated with an application to a large-scale randomized experiment.
Submitted.
