A Promising New Approach to Eliminating Selection Bias

Authors

  • Laura R. Peck School of Public Affairs, Arizona State University, Phoenix, Arizona, USA
  • Furio Camillo Dipartimento di scienze statistiche, Università di Bologna, Bologna, Italy
  • Ida D'Attoma Dipartimento di scienze statistiche, Università di Bologna, Bologna, Italy

DOI:

https://doi.org/10.3138/cjpe.24.002

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.

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Published

2009-11-21

How to Cite

Peck, L. R., Camillo, F., & D'Attoma, I. (2009). A Promising New Approach to Eliminating Selection Bias. Canadian Journal of Program Evaluation, 24(2), 31–56. https://doi.org/10.3138/cjpe.24.002

Issue

Vol. 24 No. 2 (2009): Fall

Section

Articles

License

Copyright (c) 2009 Laura R. Peck, Furio Camillo, Ida D'Attoma

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