Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect.

@article{Ghosh2018SufficientDR,
  title={Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect.},
  author={Trinetri Ghosh and Yanyuan Ma and Xavier de Luna},
  journal={Statistica Sinica},
  year={2018},
  volume={31 2},
  pages={
          821-842
        }
}
When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and non-treated groups. We then integrate all results through imputation, inverse probability weighting and double robust augmentation estimators. Double robust estimators are locally efficient while imputation estimators are super-efficient when the response models are… 

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