论文标题
估计有条件的平均治疗效果，缺少治疗信息
Estimating Conditional Average Treatment Effects with Missing Treatment Information
论文作者
论文摘要
估计有条件的平均治疗效果（CATE）具有挑战性，尤其是在缺少治疗信息的情况下。尽管这在实践中是一个普遍存在的问题，但CATE估计缺失治疗的关注很少。在本文中，我们分析了环境中的CATE估计，而缺少治疗方法是以协变量转移形式出现的独特挑战。我们在我们的环境中确定了两个协变量的转变：（i）治疗人群和对照人群之间的协变速度； （ii）观察到的治疗群体和缺失的治疗人群之间的协变。我们首先从理论上显示了这些协变量转移的效果，通过得出用于估算我们环境中缺失治疗的cate的概括。然后，在我们的界限的激励下，我们开发了缺少的治疗表示网络（MTRNET），这是一种新型的CATE估计算法，它使用域适应性地了解了协变量的平衡表示。通过使用平衡表示，MTRNET在未完全观察到数据的协变量域中提供了更可靠的CATE估计。在具有半合成和现实世界数据的各种实验中，我们表明我们的算法通过实质性的余量改善了最先进的算法。
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where unique challenges arise in the form of covariate shifts. We identify two covariate shifts in our setting: (i) a covariate shift between the treated and control population; and (ii) a covariate shift between the observed and missing treatment population. We first theoretically show the effect of these covariate shifts by deriving a generalization bound for estimating CATE in our setting with missing treatments. Then, motivated by our bound, we develop the missing treatment representation network (MTRNet), a novel CATE estimation algorithm that learns a balanced representation of covariates using domain adaptation. By using balanced representations, MTRNet provides more reliable CATE estimates in the covariate domains where the data are not fully observed. In various experiments with semi-synthetic and real-world data, we show that our algorithm improves over the state-of-the-art by a substantial margin.
