Publications by Author: Daniel E. Ho

Amici curiae are leaders in the field of quantitative social science and statistical methodology. Amici submit this brief to point out the substantial methodological flaws in the “mismatch” research discussed in the Brief for Richard Sander as Amicus Curiae in Support of Petitioner. Professor Sander’s mismatch hypothesis is unsupported and based on work that fails to adhere to basic tenets of research design.

In Grutter v. Bollinger, this Court held that a state has a compelling interest in attaining a diverse student body for the benefit of all students, and thatthis compelling interest justifies the consideration of race as a factor in university admissions. See 539 U.S. 306, 325, 328 (2003). In this, the latest case to consider the constitutionality of affirmative-action admissions policies, Professor Richard H. Sander, along with lawyer and journalist Stuart S. Taylor, Jr., filed a brief amici curiae arguing that social-science research has shown affirmative action to be harmful to minority students. See Brief Amici Curiae for Richard Sander and Stuart Taylor, Jr. in Supportof Neither Party (“Sander-Taylor Brief”) 2. According to them, a “growing volume of very careful research, some of it completely unrebutted by dissenting work” has found that affirmative-action practices are not having their intended effect. Id.; see also Brief Amici Curiae of Gail Heriot et al. in Support of Petitioner (“Three Commissioners Brief”) 14 (“The Commissioner Amici are aware of no empirical research that challenges [Sander’s] findings.”). But, as amici will show, the principal research on which Sander and Taylor rely for their conclusion about the negative effects of affirmative action—Sander’s so-called “mismatch” hypothesis2—is far from “unrebutted.” Sander-Taylor Brief 2. Since Sander first published findings in support of a“mismatch” in 2004, that research has been subjected to wide-ranging criticism. Nor is Sander’s research “very careful.” Id. As some of those critiques discussin detail, Sander’s research has major methodologicalflaws—misapplying basic principles of causal inference—that call into doubt his controversial conclusions about affirmative action. The Sander “mismatch” research—and its provocative claim that, on average, minority students admitted through affirmative action would be better off attending less selective colleges and universities—is not good social science. Sander’s research has “significantly overestimated the costs of affirmative action and failed to demonstrate benefits from ending it.” David L. Chambers et al., The Real Impact of Affirmative Action in American Law Schools: An Empirical Critique of Richard Sander’s Study, 57 Stan. L. Rev. 1855, 1857 (2005). That research, which consists of weak empirical contentions that fail to meet the basic tenets of rigorous social-science research, provides no basis for this Court to revisit longstanding precedent supporting the individualized consideration of race in admissions. Cf. Grutter, 539 U.S. at 334 (“Universities can * * * consider race or ethnicity more flexibly as a ‘plus’ factor in the context of individualized consideration of each and every applicant.”) (citing Regents of Univ. of Cal. v. Bakke, 438 U.S. 265, 315-316 (1978) (opinion of Powell, J.,)).In light of the significant methodological flaws on which it rests, Sander’s research does not constitute credible evidence that affirmative action practices are harmful to minorities, let alone that the diversity rationale at the heart of Grutter is at odds with social science.

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.