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Activity Number:
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9
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Type:
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Invited
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Consulting
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| Abstract - #303131 |
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Title:
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Double-Robust and Efficient Methods for Estimating the Causal Effects of a Binary Treatment
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Author(s):
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Andrea Rotnitzky*+ and James Robins and Mariela Sued and Quanhong Lei-Gomez
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Companies:
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Universidad Di Tella and Harvard University and Harvard School of Public Health and Universidad de Buenos Aires and Harvard School of Public Health
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Address:
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International 1428, Buenas Aires, , Argentina
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Keywords:
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counterfactual outcomes ; potential outcomes ; semiparametric regression
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Abstract:
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We consider estimation of the effects of a binary treatment on a continuous outcome of interest from observational data. We provide a new estimator of the population average treatment effect (ATE) based on the difference of novel double-robust (DR) estimators of the treatment-specific outcome means. DR-difference estimators may have poor finite sample behavior when the estimated propensity scores in the treated and untreated do not overlap. We propose an alternative approach, which can be used even in this unfavorable setting, based on locally efficient double-robust estimation of a semiparametric regression model for the modification on an additive scale of the magnitude of the treatment effect by baseline covariates X. Our approach allows estimation of ATE in the total study population, in the random subpopulation with overlapping estimated propensity scores, and within levels of X.
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