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摘要翻译：
在本文中，我们提出了使用因果推理技术来估计和预测数据的子组，直到单个单元的生存函数。树系综方法，特别是随机森林被修改为这一目的。一个真实世界的医疗保健数据集被用于大约1800名乳腺癌患者，该数据集有多个患者协变量，以及无病生存日(DFS)和死亡事件二元指标(y)。我们使用癌症治疗干预的类型作为治疗变量(t=0或1，在我们的例子中是二元治疗情况）。该算法是一个两步的方法。在步骤1中，我们使用以DFS为因变量的因果树来估计异质处理效果。接下来，在步骤2中，对于平均治疗效果明显不同（关于存活率）的因果树中的每一叶，我们对该叶中的所有患者拟合一个存活率森林，治疗t=0和t=1各一个森林，以获得每个治疗的估计患者水平存活率曲线（更一般地，任何模型都可以在此步骤中使用）。然后，我们减去患者水平生存曲线，得到给定患者的差异生存曲线，以比较两种治疗结果的生存函数。到一个选定的叶子的路径也给我们病人特征及其值的组合，这对于叶子上的治疗效果差异是重要的。
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英文标题：
《Causal Inference for Survival Analysis》
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作者：
Vikas Ramachandra
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
In this paper, we propose the use of causal inference techniques for survival function estimation and prediction for subgroups of the data, upto individual units. Tree ensemble methods, specifically random forests were modified for this purpose. A real world healthcare dataset was used with about 1800 patients with breast cancer, which has multiple patient covariates as well as disease free survival days (DFS) and a death event binary indicator (y). We use the type of cancer curative intervention as the treatment variable (T=0 or 1, binary treatment case in our example). The algorithm is a 2 step approach. In step 1, we estimate heterogeneous treatment effects using a causalTree with the DFS as the dependent variable. Next, in step 2, for each selected leaf of the causalTree with distinctly different average treatment effect (with respect to survival), we fit a survival forest to all the patients in that leaf, one forest each for treatment T=0 as well as T=1 to get estimated patient level survival curves for each treatment (more generally, any model can be used at this step). Then, we subtract the patient level survival curves to get the differential survival curve for a given patient, to compare the survival function as a result of the 2 treatments. The path to a selected leaf also gives us the combination of patient features and their values which are causally important for the treatment effect difference at the leaf.
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PDF链接：
https://arxiv.org/pdf/1803.08218
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