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

Causal diagrams for empirical legal research: a methodology for identifying causation, avoiding bias and interpreting results