Scholarship on multimethod case selection in the social sciences has developed rapidly in recent years, but many possibilities remain unexplored. This essay introduces an attractive and advantageous new alternative, involving the selection of extreme cases on the treatment variable, net of the statistical influence of the set of known control variables. Cases that are extreme in this way are those in which the value of the main causal variable is as surprising as possible, and thus, this approach can be referred to as seeking “surprising causes.” There are practical advantages to selecting on surprising causes, and there are also advantages in terms of statistical efficiency in facilitating case-study discovery. We first argue for these advantages in general terms and then demonstrate them in an application regarding the dynamics of U.S. labor legislation.

近年来，社会科学中关于多方法案例选择的奖学金发展迅速，但仍有许多可能性尚未开发。本文介绍了一种有吸引力的且有利的新替代方法，其中包括根据一组已知控制变量的统计影响来选择治疗变量上的极端情况。以这种方式极端的情况是那些主要因果变量的值尽可能令人惊讶的情况，因此，这种方法可以称为寻求“令人惊讶的原因”。选择令人惊讶的原因具有实际优势，并且在促进案例研究发现的统计效率方面也具有优势。我们首先从总体上争夺这些优势，然后在有关美国动态的应用程序中对其进行论证