Focusing solely on the methodological aspect of the recent Harvard College admissions decision, one under-appreciated take-away from the district court's opinion (click here for the full decision) is the prominent role that traditional statistical analyses (basically, standard logistic models) played in the trial. If nothing else, the prominent role that statistical evidence bearing on the plaintiffs' claims, and Harvard's defense, played in the trial makes palpably clear, once again, the extent to which sophisticated litigation now--and almost routinely so in some case types--deploys empirical evidence. Indeed, the court's decision devotes more than 40 pages (pp.50-92) of its 130-page opinion to statistical matters.
In the Harvard admissions case, both parties to the litigation formally engaged experts, Professors David Card (Berkeley) and Peter Arcidiacono (Duke), to analyze a shared data set--six years worth of Harvard College admissions decisions. Despite a shared data set the two experts reached opposing conclusions on the critical question about the unique role that applicants' race/ethnicity played in Harvard's admissions decisions. Much of the experts' disagreement pivoted on one key modeling decision--whether to include or exclude admissions data germane to the "ALDC" applications. ("ALDC" applications are those involving: "recruited athletes, legacies, Dean's 'interest list' and children of Harvard faculty and staff.")
In the face of conflicting conclusions from the two experts, Judge Burroughs wrote that while she found both models “'econometrically defensible' and preferred different aspects of each model," in the end Judge Burroughs' decision largely preferenced Card's modeling assumptions over Arcidiacono's. Independent of whether one feels Judge Burroughs' assessment of the competing modeling decisions is persuasive, what is objectively clear is that she took the empirical and statistical evidence seriously and that it played a consequential--and potentially pivotal--role in the trial.
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