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Direct linkPeck, Laura R.; Camillo, Furio; D'Attoma, Ida – Canadian Journal of Program Evaluation, 2009
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,…
Descriptors: Program Evaluation, Intervention, Experiments, Comparative Analysis
