Efficiency of the u,v method of estimation

Given a pool of motorists, how do we estimate the total intensity of those who had a prespecified number of traffic accidents in the past year? We previously have proposed the u,v method as a solution to estimation problems of this type. In this paper, we prove that the u,v method provides asymptotically efficient estimators in an important special case.

Randomised controlled trial of the READER method of critical appraisal in general practice

Objective: To evaluate the READER model for critical reading by comparing it with a free appraisal, and to explore what factors influence different components of the model.

Sources of selection bias in evaluating social programs: An interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method

This paper decomposes the conventional measure of selection bias in observational studies into three components. The first two components are due to differences in the distributions of characteristics between participant and nonparticipant (comparison) group members: the first arises from differences in the supports, and the second from differences in densities over the region of common support. The third component arises from selection bias precisely defined. Using data from a recent social experiment, we find that the component due to selection bias, precisely defined, is smaller than the first two components. However, selection bias still represents a substantial fraction of the experimental impact estimate. The empirical performance of matching methods of program evaluation is also examined. We find that matching based on the propensity score eliminates some but not all of the measured selection bias, with the remaining bias still a substantial fraction of the estimated impact. We find that the support of the distribution of propensity scores for the comparison group is typically only a small portion of the support for the participant group. For values outside the common support, it is impossible to reliably estimate the effect of program participation using matching methods. If the impact of participation depends on the propensity score, as we find in our data, the failure of the common support condition severely limits matching compared with random assignment as an evaluation estimator.