论文标题
在异质特征空间上进行治疗效果估计的转移学习
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
论文作者
论文摘要
考虑通过利用来自具有不同特征空间的源域的相关信息来改善目标域的条件平均治疗效果（CATE）的问题。在医疗保健等领域，我们可能希望评估对新患者人群的治疗有效性，用于不同的临床协变量和有限的数据可获得的新患者，因此，对于CATE估计的这种异质转移学习问题无处不在。在本文中，我们通过引入几个构建块来解决此问题，这些构件使用表示表示学习来处理异质特征空间和具有共享和私有层的灵活的多任务架构，以在跨域的潜在结果功能之间传输信息。然后，我们展示如何使用这些构建块来恢复标准CATE学习者的转移学习当量。在用于异质转移学习的新的半合成数据模拟基准上，我们不仅表明了跨数据集的异质转移因果效应学习者的绩效提高，而且还提供了从转移角度来看这些学习者之间差异的见解。
Consider the problem of improving the estimation of conditional average treatment effects (CATE) for a target domain of interest by leveraging related information from a source domain with a different feature space. This heterogeneous transfer learning problem for CATE estimation is ubiquitous in areas such as healthcare where we may wish to evaluate the effectiveness of a treatment for a new patient population for which different clinical covariates and limited data are available. In this paper, we address this problem by introducing several building blocks that use representation learning to handle the heterogeneous feature spaces and a flexible multi-task architecture with shared and private layers to transfer information between potential outcome functions across domains. Then, we show how these building blocks can be used to recover transfer learning equivalents of the standard CATE learners. On a new semi-synthetic data simulation benchmark for heterogeneous transfer learning we not only demonstrate performance improvements of our heterogeneous transfer causal effect learners across datasets, but also provide insights into the differences between these learners from a transfer perspective.
