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

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• 作者：Yeying Zhu (1)
  Debashis Ghosh (2)
  Nandita Mitra (3)
  Bhramar Mukherjee (4)
  
• 关键词：Boosting algorithms ; Causal inference ; Logistic regression ; Observational data ; Random forests
• 刊名：Health Services and Outcomes Research Methodology
• 出版年：2014
• 出版时间：September 2014
• 年：2014
• 卷：14
• 期：3
• 页码：69-91
• 全文大小：371 KB
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• 作者单位：Yeying Zhu (1)
  Debashis Ghosh (2)
  Nandita Mitra (3)
  Bhramar Mukherjee (4)
  
  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
  2. Department of Statistics, Pennsylvania State University, University Park, PA, 16802, USA
  3. Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
  4. Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
  
• ISSN：1572-9400

文摘

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.