causal-inference-missing:使用不完整的混杂因素生成因果推理模拟的代码

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因果推理缺失 该存储库包含Mayer等人的文章“的相关的代码和管道。 (2020)。 一般使用 在缺少的属性,即,不完全的混杂因素和协变量的存在估计治疗效果的充分的管道,在提供 。 该管道可以直接应用于自定义数据集(默认为模拟玩具示例),前提是它适合以下格式: X.na :混杂因素。 大小为#observations x #covariates 。 有或没有缺失值。 W :治疗分配。 用{0,1}或{FALSE,TRUE}编码的二进制向量(表示{control,treatment} )。 没有缺失的价值。 Y :观察到的结果。 数值或二进制向量(如果是二进制,则用{0,1}进行编码)。 没有缺失的价值。 应用:氨甲环酸在颅脑外伤中的作用 该方法已应用于医学问题,即药物氨甲环酸对创伤性脑损伤患者死亡率的影响。 该应用程序使用的数据是从提取的。 该注册表仅应要求提供。 但是,我们在提供了

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