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.
翻译:SDRcausal 是一个实施Ghosh、Ma和de Luna(2021年)中提议的充分减少因果推断方法的包件。包件执行(强化)反概率加权和结果回归(估计)平均治疗效果参数(ATE)的估测。Nuisance 模型,根据共差(适应性评分)和结果回归模型的处理分配概率,都通过使用半对称的当地有效减少因果推算仪来安装,从而允许大量共解的组合。技术,包括线性外推法、数字差异和轨迹,已经用于实现方法的可行实施。找到适当的减少因子图(中央平均子空间)需要解决一个优化问题,并且给用户提供几种优化算法作为选择。包件还提供因果估量器的因果差异的估测器。提供了绘图选项。方法的核心在C语言中实施,允许平行化。用户友好和自由软件库RADR4软件库用于界面。