SDRcausal

The package implements the methods described in Ghosh et al. (2020): Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect. It uses a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and nontreated groups. It then integrates all results through imputation, inverse probability weighting and doubly robust augmentation estimators.

Keyworkds: Average Treatment Effect, Doubly Robust Estimator, Efficiency, Inverse Probability Weighting, sufficient dimension reduction

Load SDRcausal package here (beta version soon available!).

References

Ghosh, T., Ma, Y. and de Luna, X. (2020). Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect. Statistica Sinica. On-line ahead of print. DOI: 10.5705/ss.202018.0416. ArXiv version with Supplementary material: here.