Avoiding Randomization Failure in Program Evaluation

Citation:

Gary King, Richard Nielsen, Carter Coberley, James E Pope, and Aaron Wells. 2011. “Avoiding Randomization Failure in Program Evaluation.” Population Health Management, 14, 1, Pp. S11-S22. Copy at http://j.mp/2oTEZI6
Article239 KB
Avoiding Randomization Failure in Program Evaluation

Abstract:

We highlight common problems in the application of random treatment assignment in large scale program evaluation. Random assignment is the defining feature of modern experimental design. Yet, errors in design, implementation, and analysis often result in real world applications not benefiting from the advantages of randomization. The errors we highlight cover the control of variability, levels of randomization, size of treatment arms, and power to detect causal effects, as well as the many problems that commonly lead to post-treatment bias. We illustrate with an application to the Medicare Health Support evaluation, including recommendations for improving the design and analysis of this and other large scale randomized experiments.

Last updated on 07/25/2013