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Implementing double-robust estimators of causal effects

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Author Info
Richard Emsley () (Biostatistics, Health Methodology Research Group, The University of Manchester)
Mark Lunt (Arthritis Research Campaign Epidemiology Unit, The University of Manchester)
Andrew Pickles (Biostatistics, Health Methodology Research Group, The University of Manchester)
GraHam Dunn (Biostatistics, Health Methodology Research Group, The University of Manchester)
Abstract

This article describes the implementation of a double-robust estimator for pretest-posttest studies (Lunceford and Davidian, 2004, Statistics in Medicine 23: 2937-2960) and presents a new Stata command (dr) that carries out the procedure. A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection effects such as confounding by allowing for different forms of model misspecification; a double-robust estimator also can offer increased efficiency when all the models are correctly specified. We demonstrate the results with a Monte Carlo simulation study, and we show how to implement the double-robust estimator on a single simulated dataset, both manually and by using the dr command. Copyright 2008 by StataCorp LP.

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Publisher Info
Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 8 (2008)
Issue (Month): 3 (September)
Pages: 334-353
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Handle: RePEc:tsj:stataj:v:8:y:2008:i:3:p:334-353

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Related research
Keywords: dr; double-robust estimators; causal models; confounding; inverse probability of treatment weights; propensity score;

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  1. Bernardo Lara & Alejandra Mizala & Andrea Repetto, 2009. "Revisiting the School Choice Debate in Chile," Documentos de Trabajo 263, Centro de Economía Aplicada, Universidad de Chile. [Downloadable!]
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This page was last updated on 2009-10-27.

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