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 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…
One Citation
Robust Estimating Method for Propensity Score Models and its Application to Some Causal Estimands: A review and proposal
- Business
- 2022
In observational study, the propensity score has the central role to estimate causal effects. Since the propensity score is usually unknown, estimating by appropriate procedures is an indispensable…
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.