title: Causal diagrams for empirical legal research: a methodology for identifying causation, avoiding bias and interpreting results.
abstract: 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.
authors: Tyler VanderWeele, PhD, is an Associate Professor in the Harvard School of Public Health, Departments of Epidemiology and Biostatistics; Nancy Staudt, JD and PhD, is the Class of 1940 Research Professor of Law at Northwestern University Law School.
authors: Tyler J
authors: TJ
authors: VanderWeele
authors: Tyler VanderWeele, PhD, is an Associate Professor in the Harvard School of Public Health, Departments of Epidemiology and Biostatistics; Nancy Staudt, JD and PhD, is the Class of 1940 Research Professor of Law at Northwestern University Law School.
authors: Nancy
authors: N
authors: Staudt