• Corpus ID: 233864391

SDRcausal: an R package for causal inference based on sufficient dimension reduction

@inproceedings{Ghasempour2021SDRcausalAR,
  title={SDRcausal: an R package for causal inference based on sufficient dimension reduction},
  author={Mohammad Reza Ghasempour and Xavier de Luna},
  year={2021}
}
2 Package installation 2 Assumptions and parameter of interest 3 Average Treatment Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Nuisance Models and Dimension Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Fitting the Propensity Score and Outcome Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Estimation of ATE 4 A Data Generating Process… 

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References

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

A shrinkage estimator is introduced to automatically combine the double robustness property while improving on the variance when the response model is correct, to take advantage of both procedures.