Implementing double-robust estimators of causal effects

Richard Emsley, Mark Lunt, Andrew Pickles, Graham Dunn

Research output: Contribution to journalArticlepeer-review

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. © 2008 StataCorp LP.
Original languageEnglish
Pages (from-to)334-353
Number of pages19
JournalThe Stata Journal
Volume8
Issue number3
Publication statusPublished - Sept 2008

Keywords

  • Causal models
  • Confounding
  • Double-robust estimators
  • Dr
  • Inverse probability of treatment weights
  • Propensity score
  • St0149

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