论文标题
通过因果人群识别估算个人治疗效应
Estimating Individual Treatment Effects through Causal Populations Identification
论文作者
论文摘要
从观察数据中估算单个治疗效果，定义为在没有治疗或干预的情况下的结果之间的差异，同时仅观察两者，这是因果学习中一个具有挑战性的问题。在本文中，我们将此问题提出为基于四个独家因果人群模型的隐藏变量的推论，并强制执行因果约束。我们提出了一种新版本的EM算法，认为是预期的 - 伴侣最大化（ECM）算法，并在轻度条件下提供了有关其收敛性的提示。我们将我们的算法与合成和现实世界数据的基线方法进行比较，并讨论其性能。
Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one of both, is a challenging problems in causal learning. In this paper, we formulate this problem as an inference from hidden variables and enforce causal constraints based on a model of four exclusive causal populations. We propose a new version of the EM algorithm, coined as Expected-Causality-Maximization (ECM) algorithm and provide hints on its convergence under mild conditions. We compare our algorithm to baseline methods on synthetic and real-world data and discuss its performances.
