期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
卷 75, 期 2, 页码 369-396出版社
WILEY-BLACKWELL
DOI: 10.1111/j.1467-9868.2012.01048.x
关键词
Blocking; Neyman; Rubin model; Randomized trials; Regression adjustment
资金
- National Science Foundation [SES-0835531]
- Army Research Office [W911NF-11-1-0114]
. Experimenters often use post-stratification to adjust estimates. Post-stratification is akin to blocking, except that the number of treated units in each stratum is a random variable because stratification occurs after treatment assignment. We analyse both post-stratification and blocking under the NeymanRubin model and compare the efficiency of these designs. We derive the variances for a post-stratified estimator and a simple difference-in-means estimator under different randomization schemes. Post-stratification is nearly as efficient as blocking: the difference in their variances is of the order of 1/n2, with a constant depending on treatment proportion. Post-stratification is therefore a reasonable alternative to blocking when blocking is not feasible. However, in finite samples, post-stratification can increase variance if the number of strata is large and the strata are poorly chosen. To examine why the estimators' variances are different, we extend our results by conditioning on the observed number of treated units in each stratum. Conditioning also provides more accurate variance estimates because it takes into account how close (or far) a realized random sample is from a comparable blocked experiment. We then show that the practical substance of our results remains under an infinite population sampling model. Finally, we provide an analysis of an actual experiment to illustrate our analytical results.
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