The Issue: People attempt to draw causal inferences using many different statistical techniques (e.g. regression, propensity score matching, regression discontinuity, instrumental variables, etc.). One great way to learn about the strengths and weaknesses of different statistical techniques for causal inference is to compare them on the same data. Since randomized experiments are the so called "gold standard" for causal inference, they are obviously an excellent benchmark.
I have seen several studies of this last type, but I could only recall two. LaLonde's classic: "Evaluating the Econometric Evaluations of Training Programs with Experimental Data" and Aiken et. al. "Comparison of a Randomized and Two Quasi-Experimental Designs in a Single Outcome Evaluation: Efficacy of a University-Level Remedial Writing Program."
Do you know of other examples of this type of study?