Browsing by Subject "Objectivity"

Now showing 1 - 2 of 2
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    Free Speech and Antisemitism: Collin v. Smith Today
    (Elsevier, 2021-09-08) Wright, R. George; Robert H. McKinney School of Law
    The Skokie-based Collin v. Smith litigation resulted in our law's most significant constitutional response to antisemitic hate speech. The Skokie case opinions shed light on how antisemitism was thought of at the time and place in question. More importantly, how we choose now to understand the Collin v. Smith cases tells us much about how we conceive of antisemitism and of antisemitic injury today. The argument herein is that our understanding of freedom of speech, and of its value and limits, has significantly evolved over the decades since Collin v. Smith. Relatedly, our collective understanding of the harms and injuries inflicted by antisemitic speech has, at the deepest level, been significantly changing as well. In both of these respects, the Collin v. Smith litigation has only increased in importance over time.
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    Inverse probability weighting for covariate adjustment in randomized studies
    (Wiley, 2014-02) Shen, Changyu; Li, Xiaochun; Li, Lingling; Biostatistics, School of Medicine
    Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented.