This article explains the usage of R package CausalModels, which is publicly available on the Comprehensive R Archive Network. While packages are available for sufficiently estimating causal effects, there lacks a package that provides a collection of structural models using the conventional statistical approach developed by Hernan and Robins (2020). CausalModels addresses this deficiency of software in R concerning causal inference by offering tools for methods that account for biases in observational data without requiring extensive statistical knowledge. These methods should not be ignored and may be more appropriate or efficient in solving particular problems. While implementations of these statistical models are distributed among a number of causal packages, CausalModels introduces a simple and accessible framework for a consistent modeling pipeline among a variety of statistical methods for estimating causal effects in a single R package. It consists of common methods including standardization, IP weighting, G-estimation, outcome regression, instrumental variables and propensity matching.

翻译：本文介绍了R包CausalModels的使用方法，该包已在综合R存档网络公开提供。尽管现有软件包足以估计因果效应，但目前缺乏一个基于Hernan和Robins（2020）传统统计方法的结构模型集合。CausalModels通过提供无需深厚统计知识即可处理观测数据偏差的方法工具，弥补了R语言在因果推断方面的这一软件缺陷。这些方法不应被忽视，且在解决特定问题时可能更为适用或高效。虽然这些统计模型的实现分散于多个因果推断软件包中，但CausalModels为单一R包中估计因果效应的多种统计方法提供了一个简洁易用且一致的建模框架。该包包含标准化、逆概率加权、G估计、结果回归、工具变量及倾向性匹配等常用方法。