CIMTx provides efficient and unified functions to implement modern methods for causal inferences with multiple treatments using observational data with a focus on binary outcomes. The methods include regression adjustment, inverse probability of treatment weighting, Bayesian additive regression trees, regression adjustment with multivariate spline of the generalized propensity score, vector matching and targeted maximum likelihood estimation. In addition, CIMTx illustrates ways in which users can simulate data adhering to the complex data structures in the multiple treatment setting. Furthermore, the CIMTx package offers a unique set of features to address the key causal assumptions: positivity and ignorability. For the positivity assumption, CIMTx demonstrates techniques to identify the common support region for retaining inferential units using inverse probability of treatment weighting, Bayesian additive regression trees and vector matching}. To handle the ignorability assumption, CIMTx provides a flexible Monte Carlo sensitivity analysis approach to evaluate how causal conclusions would be altered in response to different magnitude of departure from ignorable treatment assignment.

翻译：CIMTx提供高效和统一的功能,以便利用观测数据,以二元结果为重点,对多重处理采用现代因果推断方法,这些方法包括回归调整、治疗权重的反概率、巴伊西亚累加回归树、以通用偏差分多变量样条的回归调整、矢量匹配和有针对性的最大概率估算。此外,CIMTx还说明了用户如何模拟数据,以遵守多重处理环境中的复杂数据结构。此外,CIMTx软件包提供了一套独特的特征,以解决关键的因果假设:即假设和可忽略性。关于假设,CIMTx展示了利用治疗权重度的反概率、巴伊西亚累加回归树和矢量匹配来确定保留推断单位的共同支持区域的技术。为了处理可忽略性假设,CIMTx提供了灵活的蒙特卡洛灵敏度分析方法,以评价因不同程度的偏离可忽略的治疗任务而改变因果结论的方式。