SDRcausal: an R package for causal inference based on sufficient dimension reduction

TL;DR

SDRcausal is an R package that applies sufficient dimension reduction techniques to causal inference, enabling efficient estimation of average treatment effects with high-dimensional covariates using advanced optimization and semiparametric methods.

Contribution

The package introduces a practical implementation of dimension reduction-based causal inference methods, including optimization algorithms and variance estimation, tailored for large covariate sets.

Findings

Allows for large confounder sets in causal analysis

Provides efficient estimators with variance measures

Supports parallel computation for scalability

Abstract

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.