swMATH ID: 38424
Software Authors: Mohammad Ghasempour, Xavier de Luna
Description: SDRcausal: an R package for causal inference based on sufficient dimension reduction. SDRcausal is a package that implements sufficient dimension reduction methods for causal inference as proposed in Ghosh, Ma, and de Luna (2021). The package implements (augmented) inverse probability weighting and outcome regression (imputation) estimators of an average treatment effect (ATE) parameter. Nuisance models, both treatment assignment probability given the covariates (propensity score) and outcome regression models, are fitted by using semiparametric locally efficient dimension reduction estimators, thereby allowing for large sets of confounding covariates. Techniques including linear extrapolation, numerical differentiation, and truncation have been used to obtain a practicable implementation of the methods. Finding the suitable dimension reduction map (central mean subspace) requires solving an optimization problem, and several optimization algorithms are given as choices to the user. The package also provides estimators of the asymptotic variances of the causal effect estimators implemented. Plotting options are provided. The core of the methods are implemented in C language, and parallelization is allowed for. The user-friendly and freeware R language is used as interface. The package can be downloaded from Github repository: https://github.com/stat4reg
Homepage: https://arxiv.org/abs/2105.02499
Source Code:  https://github.com/stat4reg/SDRcausal
Dependencies: R
Keywords: Methodology; arXiv_stat.ME; R; R package; causal inference; sufficient dimension reduction
Related Software: devtools; R
Cited in: 0 Documents

Standard Articles

1 Publication describing the Software Year
SDRcausal: an R package for causal inference based on sufficient dimension reduction arXiv
Mohammad Ghasempour, Xavier de Luna