论文标题
使用机器学习估计干预措施在连续变量中的平均因果效应
Estimating the average causal effect of intervention in continuous variables using machine learning
论文作者
论文摘要
估计平均因果效应/平均治疗效果的最广泛讨论的方法是干预的分离二进制变量的方法，其价值代表干预/不干预组。另一方面，尚未开发出独立于数据生成模型的连续变量的方法。在这项研究中，我们提供了一种估计连续变量干预的平均因果效应的方法，只要可因果效应可识别，该方法可以应用于任何生成模型的数据。提出的方法与机器学习算法无关，并保留数据的可识别性。
The most widely discussed methods for estimating the Average Causal Effect/Average Treatment Effect are those for intervention in discrete binary variables whose value represents intervention/non-intervention groups. On the other hand, methods for intervening in continuous variables independent of data generating models have not been developed. In this study, we give a method for estimating the average causal effect for intervention in continuous variables that can be applied to data of any generating models as long as the causal effect is identifiable. The proposing method is independent of machine learning algorithms and preserves the identifiability of data.
