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…Â
References
Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect.
- MathematicsStatistica Sinica
- 2021
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.