 Block What You Can, Except When You Shouldn’tNicole E. Pashley and
Luke W. Miratrix
Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 1, 69-100 Abstract: Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the precision of estimates, and some have concluded it can hurt. In this article, we reconcile these apparently conflicting views, give a more thorough discussion of what guarantees no harm, and discuss how other aspects of a blocked design can cost, all in terms of estimator precision. We discuss how the different findings are due to different sampling models and assumptions of how the blocks were formed. We also connect these ideas to common misconceptions; for instance, we show that analyzing a blocked experiment as if it were completely randomized, a seemingly conservative method, can actually backfire in some cases. Overall, we find that blocking can have a price but that this price is usually small and the potential for gain can be large. It is hard to go too far wrong with blocking. Keywords: causal inference; potential outcomes; precision; finite-sample inference; randomization inference; Neymanian inference (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:1:p:69-100 DOI: 10.3102/10769986211027240 Access Statistics for this article More articles in Journal of Educational and Behavioral Statistics |
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