  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 paper, 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 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 showing 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.
