论文标题
阻止您可以的东西，除非您不应该
Block what you can, except when you shouldn't
论文作者
论文摘要
潜在结果因果推论文献的几个分支已经讨论了阻止与完全随机分组的优点。有些人得出结论，它永远不会损害估计的精度，有些人得出结论可能会伤害估计。在本文中，我们调和了这些显然相互矛盾的观点，对保证没有伤害的是更彻底的讨论，并讨论被阻塞的设计的其他方面如何在精确的方面花费。我们讨论如何由于不同的采样模型以及对块的形成方式的假设而引起的。我们还将这些想法与常见的误解联系起来，例如表明分析被阻止的实验，好像它是完全随机的，看似保守的方法在某些情况下实际上可能适得其反。总体而言，我们发现阻止可以有一个价格，但是这个价格通常很小，而且收益的潜力可能很大。阻止很难错过。
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.
