Civil rights litigation often concerns the causal effect of some characteristic on decisions made by a governmental or socioeconomic actor. An analyst may be interested, for example, in the effect of victim race on jury imposition of the death penalty, in the effect of applicant gender on a firm’s hiring decisions, or in the effect of candidate ethnicity on election results. For the past thirty years, such analyses have primarily been accomplished via a statistical technique known as regression. But as it has been used in civil rights litigation, regression suffers from several shortcomings: it facilitates biased, result-oriented thinking by expert witnesses; it encourages judges and litigators to believe that all questions are equally answerable; and it gives the wrong answer in situations in which such might be avoided. These difficulties, and several others, all stem from the fact that regression does not begin with a paradigm for defining causal effects and for drawing causal inferences. This Article argues for a wholesale change in thinking in this area, from a focus on regression coefficients to an explicit framework of causation called “potential outcomes.” The potential outcomes paradigm of causal inference, which (for lawyers) may be analogized to but-for causation with a renewed emphasis on time, addresses many of the shortcomings of regression as the latter is currently used in civil rights litigation, and it does so within a framework courts, litigators, and juries can understand. This Article explains regression and the potential outcomes paradigm and discusses the latter’s application in the death penalty, employment discrimination, and redistricting settings
Eleventh Circuit Holds that a Florida Jail Was Not Deliberately Indifferent to the Spread of COVID-19.