In this paper we introduce methodology-causal directed acyclic graphs-that empirical researchers can use to identify causation, avoid bias, and interpret empirical results. This methodology has become popular in a number of disciplines, including statistics, biostatistics, epidemiology and computer science, but has yet to appear in the empirical legal literature. Accordingly we outline the rules and principles underlying this new methodology and then show how it can assist empirical researchers through both hypothetical and real-world examples found in the extant literature. While causal directed acyclic graphs are certainly not a panacea for all empirical problems, we show they have potential to make the most basic and fundamental tasks, such as selecting covariate controls, relatively easy and straightforward.

中文翻译：

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实证法律研究的因果图：确定因果关系、避免偏见和解释结果的方法

在本文中，我们介绍了方法论——因果有向无环图——实证研究人员可以使用它来识别因果关系、避免偏见和解释实证结果。这种方法论已经在许多学科中流行起来，包括统计学、生物统计学、流行病学和计算机科学，但尚未出现在实证法律文献中。因此，我们概述了这种新方法背后的规则和原则，然后展示了它如何通过现有文献中的假设和现实例子来帮助实证研究人员。虽然因果有向无环图肯定不是解决所有经验问题的灵丹妙药，但我们表明它们有潜力使最基本和最基本的任务（例如选择协变量控制）相对容易和直接。