Document Type


Repository Date



Courts, Methodology, Empirical Studies, Judging

Subject Categories

Courts | Judges | Law | Legal Studies


In this Response, we use Professors Cox and Miles' recent study of judicial decision-making to explore what is at stake when legal scholars present empirical findings without fully investigating the structural relationships of their data or without explicitly stating the assumptions being made to draw causal inferences. We then introduce a new methodology that is intuitive, easy to use, and, most importantly, allows scholars systematically to assess problems of bias and confounding. This methodology—known as causal directed acyclic graphs—will help empirical researchers to identify true cause and effect relationships when they exist and, at the same time, posit statistical models with appropriate controls, in order to better justify causal claims. While this methodology has become popular in a number of disciplines-- including statistics, biostatistics, epidemiology, and computer science--and is widely believed to be a valuable tool for empirical research, it has yet to appear in the empirical law literature. Accordingly, our goal is to offer a brief introduction of the method and to initiate discussion as to its worth in empirical legal studies.