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.

CBRNet 是一种用于估计观察数据中个体剂量响应的因果机器学习方法，通过平衡表示学习对连续值治疗剂量下选择偏差的克服来准确学习治疗效果，并在新提出的基准测试中表现出与其他最先进方法相当的性能。