Treatment effect estimates from a "regression discontinuity design" (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the…

Although evaluators often use an interrupted time series (ITS) design to test hypotheses about program effects, there are few empirical tests of the design's validity. We take a randomized experiment on an educational topic and compare its effects to those from a comparative ITS (CITS) design that uses the same treatment group as the experiment…

In this article, we test an alternative approach to creating a counterfactual basis for estimating individual and average treatment effects. Instead of using control/comparison groups or before-measures, the so-called Counterfactual as Self-Estimated by Program Participants (CSEPP) relies on program participants' self-estimations of their own…

There is a long-standing debate as to whether nonexperimental estimators of causal effects of social programs can overcome selection bias. Most existing reviews either are inconclusive or point to significant selection biases in nonexperimental studies. However, many of the reviews, the so-called "between-studies," do not make direct…

Drug studies are often cited as the best exemplars of evaluation design. However, many of these studies are seriously biased in favor of positive findings for the drugs evaluated, even to the point where dangerous effects are hidden. In spite of using randomized designs and double blinding, drug companies have found ways of producing the results…