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