Estimating the effects of treatments with an associated dose on an instance's outcome, the "dose response", is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such effects, also known as continuous-valued treatment effects, are typically estimated from observational data, which may be subject to dose selection bias. This means that the allocation of doses depends on pre-treatment covariates. Previous studies have shown that conventional machine learning approaches fail to learn accurate individual estimates of dose responses under the presence of dose selection bias. In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data. CBRNet adopts the Neyman-Rubin potential outcome framework and extends the concept of balanced representation learning for overcoming selection bias to continuous-valued treatments. Our work is the first to apply representation balancing in a continuous-valued treatment setting. We evaluate our method on a newly proposed benchmark. Our experiments demonstrate CBRNet's ability to accurately learn treatment effects under selection bias and competitive performance with respect to other state-of-the-art methods.

翻译：估计与剂量相关的处理对实例结果的影响，即“剂量反应”，在医疗、商业、经济学等多个领域都具有重要意义。这类效应也称为连续值处理效应，通常从观测数据中估计，而观测数据可能受到剂量选择偏差的影响，即剂量的分配取决于治疗前的协变量。先前研究表明，在存在剂量选择偏差的情况下，传统机器学习方法无法准确学习个体剂量反应的估计值。在本文中，我们提出CBRNet，一种用于从观测数据中估计个体剂量反应的因果机器学习方法。CBRNet采用Neyman-Rubin潜在结果框架，并将克服选择偏差的平衡表征学习概念扩展到连续值处理。本研究首次在连续值处理设置中应用表征平衡。我们在新提出的基准测试上评估了该方法。实验表明，CBRNet能够在选择偏差下准确学习处理效应，并在与其他先进方法的对比中展现出具有竞争力的性能。