Log In Sign Up

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

by   Mohammad Ghasempour, et al.

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:


Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect

When estimating the treatment effect in an observational study, we use a...

itdr: An R package of Integral Transformation Methods to Estimate the SDR Subspaces in Regression

Sufficient dimension reduction (SDR) is a successful tool in regression ...

Robust inference of conditional average treatment effects using dimension reduction

It is important to make robust inference of the conditional average trea...

Outcome regression-based estimation of conditional average treatment effect

The research is about a systematic investigation on the following issues...

Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression

We study the benign overfitting theory in the prediction of the conditio...

Uplift Regression: The R Package tools4uplift

Uplift modeling aims at predicting the causal effect of an action such a...

orthoDr: semiparametric dimension reduction via orthogonality constrained optimization

orthoDr is a package in R that solves dimension reduction problems using...