Estimating the individuals' potential response to varying treatment doses is
crucial for decision-making in areas such as precision medicine and management
science. Most recent studies predict counterfactual outcomes by learning a
covariate representation that is independent of the treatment variable.
However, such independence constraints neglect much of the covariate
information that is useful for counterfactual prediction, especially when the
treatment variables are continuous. To tackle the above issue, in this paper,
we first theoretically demonstrate the importance of the balancing and
prognostic representations for unbiased estimation of the heterogeneous
dose-response curves, that is, the learned representations are constrained to
satisfy the conditional independence between the covariates and both of the
treatment variables and the potential responses. Based on this, we propose a
novel Contrastive balancing Representation learning Network using a partial
distance measure, called CRNet, for estimating the heterogeneous dose-response
curves without losing the continuity of treatments. Extensive experiments are
conducted on synthetic and real-world datasets demonstrating that our proposal
significantly outperforms previous methods.

对于决策制定来说，估计个体对不同治疗剂量的潜在反应至关重要，尤其在精准医学和管理科学领域。本文通过学习与治疗变量独立的协变量表示来预测反事实结果，然而，这种独立性约束忽略了对于反事实预测有用的大量协变量信息，特别是在治疗变量是连续的情况下。为了解决这个问题，本文首先从理论上证明了平衡和预测表示在无偏估计异质剂量响应曲线方面的重要性，即学习得到的表示需满足协变量与两个治疗变量及潜在反应之间的条件独立性。基于此，我们提出了一种新颖的对比平衡表示学习网络，使用部分距离度量，称为 CRNet，用于估计异质剂量响应曲线，同时不丢失治疗剂量的连续性。我们在合成和真实世界数据集上进行了广泛实验，证明了我们的方法显著优于之前的方法。

异质剂量 - 反应曲线估计的对比平衡表示学习

Contrastive Balancing Representation Learning for Heterogeneous  Dose-Response Curves Estimation

Efficient planning of scarce resources in hospitals is a challenging task for
which a large variety of Operations Research and Management Science approaches
have been developed since the 1950s. While efficient planning of single
resources such as operating rooms, beds, or specific types of staff can already
lead to enormous efficiency gains, integrated planning of several resources has
been shown to hold even greater potential, and a large number of integrated
planning approaches have been presented in the literature over the past
decades.
This paper provides the first literature review that focuses specifically on
the Operations Research and Management Science literature related to integrated
planning of different resources in hospitals. We collect the relevant
literature and analyze it regarding different aspects such as uncertainty
modeling and the use of real-life data. Several cross comparisons reveal
interesting insights concerning, e.g., relations between the modeling and
solution methods used and the practical implementation of the approaches
developed. Moreover, we provide a high-level taxonomy for classifying different
resource-focused integration approaches and point out gaps in the literature as
well as promising directions for future research.

文章介绍了针对医院资源综合规划的运筹学及管理科学文献综述，分析不同方面的研究，提出分类和未来研究方向。