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.

潜在结果因果推断文献的几个分支已经讨论了阻断与完全随机化的优点。有些人得出结论，它永远不会损害估计的准确性，而另一些人则得出结论，它可能会受到伤害。在本文中，我们调和了这些明显相互矛盾的观点，更深入地讨论了保证无害的因素，并讨论了阻塞式设计的其他方面的成本，所有这些都涉及估算器的精度。我们讨论了不同的发现是如何由于不同的采样模型和块是如何形成的假设造成的。我们还将这些想法与常见的误解联系起来；例如，我们展示了分析一个被阻止的实验，就好像它是完全随机的一样，一种看似保守的方法，实际上在某些情况下会适得其反。全面的，我们发现阻塞是有代价的，但这个代价通常很小，而获利的潜力却很大。阻塞很难出错。