Health Services and Outcomes Research Methodology

, Volume 14, Issue 3, pp 69–91

A data-adaptive strategy for inverse weighted estimation of causal effects

  • Yeying Zhu
  • Debashis Ghosh
  • Nandita Mitra
  • Bhramar Mukherjee

DOI: 10.1007/s10742-014-0124-y

Cite this article as:
Zhu, Y., Ghosh, D., Mitra, N. et al. Health Serv Outcomes Res Method (2014) 14: 69. doi:10.1007/s10742-014-0124-y


In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we propose a weighted estimator combining parametric and nonparametric models. Some theoretical results regarding consistency of the procedure are given. Simulation studies are used to assess the performance of the newly proposed methods relative to existing methods, and a data analysis example from the Surveillance, Epidemiology and End Results database is presented.


Boosting algorithms Causal inference Logistic regression Observational data Random forests 

Supplementary material

10742_2014_124_MOESM1_ESM.tex (10 kb)
Supplementary material 1 (tex 9 KB)

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yeying Zhu
    • 1
  • Debashis Ghosh
    • 2
  • Nandita Mitra
    • 3
  • Bhramar Mukherjee
    • 4
  1. 1.Department of Statistics and Actuarial ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Department of StatisticsPennsylvania State UniversityUniversity ParkUSA
  3. 3.Department of Biostatistics and EpidemiologyUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Department of BiostatisticsUniversity of MichiganAnn ArborUSA

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