Difference-in-differences (DID) strategies are particularly useful for evaluating policy effects in natural experiments in which, for example, a policy affects some schools and students but not others. However, the standard DID method may produce biased estimation of the policy effect if the confounding effect of concurrent events varies by individual characteristics and if the experimental group and the comparison group differ in such characteristics. This paper reviews the existing alternative DID methods and compares their identification assumptions with those of the new prognostic score-based DID strategy. The author hypothesize that, in comparison with other existing DID methods, the new prognostic score-based DID strategy invokes assumptions that are relatively more plausible and is more likely to produce unbiased and efficient estimates of policy effects. Empirical findings obtained from simulation studies will inform the understanding of the relative performance of the new prognostic score-based DID method in comparison with the existing DID methods under a wide array of scenarios often plausible in educational policy evaluations with accountability data. The results will contribute to the statistics and econometrics literature and will provide practical guidance for applied researchers.