A promising new approach to eliminating selection bias

Abstract

This article presents a creative and practical process for dealing with the problem of selection bias. Taking an algorithmic approach and capitalizing on the known treatment-associated variance in the X matrix, we propose a data transformation that allows estimating unbiased treatment effects. The approach does not call for modelling the data, based on underlying theories or assumptions about the selection process, but instead calls for using the existing variability within the data and letting the data speak. We illustrate with an application of the method to Italian Job Centres. Copyright © 2007 Canadian Evaluation Society.