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    Observation of Single Top Quark Production at D0

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    This paper presents the observation of the electroweak production of single top quarks in the D0 detector at the Fermilab Tevatron Collider at a center-of-mass energy of 1.96 TeV. Events containing an isolated electron or muon and missing transverse energy, together with jets originating from the fragmentation of b quarks are used to measure a cross section for single top quark production of sigma(ppbar -> tb + X, tqb + X) = 3.94 +- 0.88 pb. The probability to measure a cross section at this value or higher in the absence of signal is 2.5X10^-7, corresponding to a 5.0 standard deviation significance.Comment: 4 pages, 2 figures; Proceedings paper of SUSY 2009 conference, Boston, MA, USA, June 2009 (to be published in AIP Conf.Proc.

    Propensity Score Analysis with Matching Weights

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    The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method resembles propensity score matching but offers a number of new features including efficient estimation, rigorous variance calculation, simple asymptotics, statistical tests of balance, clearly identified target population with optimal sampling property, and no need for choosing matching algorithm and caliper size. In addition, we propose the mirror histogram as a useful tool for graphically displaying balance. The method also shares some features of the inverse probability weighting methods, but the computation remains stable when the propensity scores approach 0 or 1. An augmented version of the matching weight estimator is developed that has the double robust property, i.e., the estimator is consistent if either the outcome model or the propensity score model is correct. In the numerical studies, the proposed methods demonstrated better performance than many widely used propensity score analysis methods such as stratification by quintiles, matching with propensity scores, and inverse probability weighting
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