This article contributes to the growing literature challenging the general assumption of and reliance on random judicial assignment by identifying common court procedures and practices that threaten unbiased causal inference. These “de‐randomizing” events, which include differing probabilities of assignment, post‐assignment judicial changes, nonrandom missingness, and nonrandom assignment itself, should be accounted for when making causal claims but are commonly either ignored or not even recognized by researchers utilizing random judicial assignment. The article explores how these de‐randomizing events violate the key empirical assumptions underlying randomized studies and offers methodological solutions. It also presents original data from a survey of the 30 largest U.S. state‐level criminal courts, outlining their assignment protocols and identifying the extent to which they feature the de‐randomizing events described in the article.

中文翻译：

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预先考虑的随机性：使用随机司法分配时识别和解释“非随机化”事件

本文通过确定威胁无偏因果推理的通用法院程序和惯例，为不断增长的挑战性挑战一般假设和依赖随机司法分配的文献做出了贡献。这些“去随机化”事件，包括不同的分配概率，分配后的司法变更，非随机缺失和非随机分配本身，应在提出因果主张时予以考虑，但通常会被研究人员忽略，甚至没有被利用随机性的研究人员所认识司法任务。本文探讨了这些去随机化事件如何违反随机研究背后的关键经验假设，并提供了方法论上的解决方案。它还提供了来自对美国30个最大的州级刑事法院进行调查的原始数据，