论文标题
估计剂量的有条件平均治疗效应的概括范围和算法
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
论文作者
论文摘要
我们研究了从观察数据和对基础系统因果关系的假设结合的治疗方法对的条件平均因果关系的任务。对于需要治疗剂对以做出决定的流行病学或经济学等研究领域，这是一个长期的挑战，但可能无法进行随机试验以精确量化其跨个体的效果和异质性。在本文中，我们扩展（Shalit等，2017）在连续剂量参数的上下文中为反事实概括误差提供了新的界限，该参数依赖于定义反事实和分配偏差调整的不同方法。然后，该结果指导了可以用来训练表示表示算法的新学习目标的定义，我们在几个基准数据集中显示了有关此问题的经验上最新的最先进的性能结果，包括与双重稳定估计方法相比。
We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying system. This has been a longstanding challenge for fields of study such as epidemiology or economics that require a treatment-dosage pair to make decisions but may not be able to run randomized trials to precisely quantify their effect and heterogeneity across individuals. In this paper, we extend (Shalit et al, 2017) to give new bounds on the counterfactual generalization error in the context of a continuous dosage parameter which relies on a different approach to defining counterfactuals and assignment bias adjustment. This result then guides the definition of new learning objectives that can be used to train representation learning algorithms for which we show empirically new state-of-the-art performance results across several benchmark datasets for this problem, including in comparison to doubly-robust estimation methods.
