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Impact of Outcome Model Misspecification on Regression and Doubly-Robust Inverse Probability Weighting to Estimate Causal Effect

Author

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  • Lefebvre Geneviève

    (Université du Québec à Montréal)

  • Gustafson Paul

    (University of British Columbia)

Abstract

Estimating treatment effects with observational data requires adjustment for confounding at the analysis stage. This is typically done by including the measured confounders along with the treatment covariate into a regression model for the outcome. Alternatively, it is also possible to adjust for confounding by taking into account the propensity of an individual to receive treatment, with inverse probability weighting (IPW). In the class of IPW estimators, the so-called doubly-robust estimator also requires the specification of the outcome regression model, in addition to the propensity model. The aim of this paper is to investigate the impact of misspecification of the outcome model on the performances of the usual regression and doubly-robust IPW estimators for estimating treatment effects. We examine the performances of the estimators across the parameter space for different scenarios of model misspecification using large-sample theory. We find that for small-to-moderate sample sizes, the regression estimator compares favorably to the IPW doubly-robust estimator. Finally we argue, both conceptually and on the basis of our results, that treatment-confounder interactions should be included in the outcome regression model.

Suggested Citation

  • Lefebvre Geneviève & Gustafson Paul, 2010. "Impact of Outcome Model Misspecification on Regression and Doubly-Robust Inverse Probability Weighting to Estimate Causal Effect," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-27, March.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:15
    DOI: 10.2202/1557-4679.1207
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    References listed on IDEAS

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    1. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
    2. Juxin Liu & Paul Gustafson, 2008. "On Average Predictive Comparisons and Interactions," International Statistical Review, International Statistical Institute, vol. 76(3), pages 419-432, December.
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    Cited by:

    1. Radice Rosalba & Ramsahai Roland & Grieve Richard & Kreif Noemi & Sadique Zia & Sekhon Jasjeet S., 2012. "Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-45, August.
    2. Gustafson Paul, 2012. "Double-Robust Estimators: Slightly More Bayesian than Meets the Eye?," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-15, January.
    3. Bartolucci, Francesco & Grilli, Leonardo & Pieroni, Luca, 2012. "Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator," MPRA Paper 43430, University Library of Munich, Germany.

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