Title: Variance Identification and Efficiency Analysis in Randomized Experiments under the Matched-Pair Design Authors: Kosuke Imai Entrydate: 2007-07-17 20:47:21 Keywords: Average Treatment Effect, Causal Inference, Experimental Design, Matched Samples, Paired Comparison, Randomization Inference. Abstract: In his landmark article, Neyman (1923) introduced randomization-based inference in analyzing experiments under the completely randomized design. Under this framework, Neyman considered the statistical estimation of the sample average treatment effect and derived the variance of the standard estimator using the treatment assignment mechanism as the sole basis of inference. In this paper, I extend Neyman's analysis to randomized experiments under the matched-pair design where experimental units are paired based on their pre-treatment characteristics and the randomization of treatment is subsequently conducted within each matched pair. I study the variance identification for the standard estimator of average treatment effects and analyze the relative efficiency of the matched-pair design over the completely randomized design. I also show how to empirically evaluate the relative efficiency of the two designs using experimental data obtained under the matched-pair design. My randomization-based analysis clarifies some of the important questions raised in the literature and identifies a hiden and yet implausible assumption that is made for the efficiency analysis in a widely used textbook. Finally, the analytical results are illustrated with numerical and empirical examples. http://polmeth.wustl.edu/retrieve.php?id=711