# 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

The SDRcausal package can be found here: github.com/stat4reg/SDRcausal

To install and load this package in R from GitHub, run the following commands:

`install.packages("devtools")`

`library(devtools) `

`install_github("stat4reg/SDRcausal")`

`library(SDRcausal)`

(all feedback welcome: mohammad.ghasempour AT umu.se)

**Mac user** can install the package using the following .gz: SDRpagckage.tar.gz

Note that this version does not use parallelization and may be somewhat slower. Mac users may also need to run the following command in a terminal window before installing the package:

`xcode-select --install`

### 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.