Free access to 'Causal inference of interaction effects'

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  • Author: Joseph Kang, Xiaogang Su, Lei Liu, Martha L. Daviglus and Statistics Views
  • Date: 19 May 2014

Each week, we select an article hot off the press and provide free access for a limited period. This week's is from Statistical Analysis and Data Mining, available from Early View, whereby articles are available to read online prior to issue allocation.

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Causal inference of interaction effects with inverse propensity weighting, G-computation and tree-based standardization
Joseph Kang, Xiaogang Su, Lei Liu and Martha L. Daviglus

Statistical Analysis and Data Mining, Early View.
DOI: 10.1002/sam.11220

 

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Given the recent interest of subgroup-level studies and personalized medicine, health research with causal inference has been developed for interaction effects of measured confounders. In estimating interaction effects, the inverse of the propensity weighting (IPW) method has been widely advocated despite the immediate availability of other competing methods such as G-computation estimates. This paper compares the advocated IPW method, the G-computation method, and our new Tree-based standardization method, which we call the Interaction effect Tree (IT). The IT procedure uses a likelihood-based decision rule to divide the subgroups into homogeneous groups where the G-computation can be applied. Our simulation studies indicate that the IT-based method along with the G-computation works robustly while the advocated IPW method needs some caution in its weighting. The authors applied the IT-based method to assess the effect of being overweight or obese on coronary artery calcification (CAC) in the Chicago Healthy Aging Study cohort.

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