This paper argues that estimating causal effects on US Appellate Court panels can be advanced by analyzing the data as a series of natural experiments, fully exploiting the as-if random assignment of judges to cases. As a template, this paper reanalyzes Boyd et al.'s data on sex-discrimination cases. The question is the impact on the votes by male judges from having a female judge on their panel. Leverage from as-if random assignment can be exploited only by restricting comparisons of treatments cases (in the example, female co-panelist) exclusively to control cases (all-male panels) from the same period and time period from which the treatment cases are drawn. With as-if random assignment reducing the possibility of a biased estimate, the results confirms a gender panel effect similar in size to the claim by Boyd et al. Restricting comparisons to within the same circuit and time period further advances understanding of the causal mechanism. When male or female judges side with female plaintiffs, the females are more persuasive at swaying the votes of their male co-panelists' votes.

中文翻译：

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作为自然实验的上诉法院分配：性别歧视案件中的性别小组效应

本文认为，可以通过将数据分析为一系列自然实验来推进对美国上诉法院小组的因果影响的估计，充分利用法官对案件的随机分配。作为模板，本文重新分析了 Boyd 等人关于性别歧视案件的数据。问题是在他们的小组中有一名女法官对男性法官的选票产生影响。仅通过将治疗病例（在示例中为女性共同小组成员）的比较仅限于与治疗病例所在的同一时期和时间段的对照病例（全男性小组）进行比较，才能利用仿佛随机分配的杠杆作用画。假设随机分配减少了有偏估计的可能性，结果证实了性别面板效应的大小与 Boyd 等人的主张相似。将比较限制在同一回路和时间段内，进一步加深了对因果机制的理解。当男性或女性法官站在女性原告一边时，女性在影响男性共同小组成员的选票方面更有说服力。