DRAFT VERSION
Abstract: This paper presents a creative and practical approach to 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 modeling the data, based on underlying theories or assumptions about the selection process, but instead it 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 Centers.