论文标题
平衡重量框架，用于估计一般治疗的因果效应
A Balancing Weight Framework for Estimating the Causal Effect of General Treatments
论文作者
论文摘要
在观察性研究中，最近可以直接优化治疗和协变量之间的平衡的加权方法受到了很多关注。但是，这些主要集中于二元治疗。受域适应的启发，我们表明可以将这种方法实际上重新重新重新制定，因为旨在解决从观察性数据转移到介入数据的差异最小化问题的特定实现。更确切地说，我们通过差异最小化（CBDM）引入了一个新的框架，协变量平衡，事实证明，该框架涵盖了大多数现有的平衡权重方法，并将其正式扩展到任意类型的处理（例如，连续或多变量）。我们为我们的框架建立了理论保证，两者都提供了治疗是二进制时已知的属性的概括，并且可以更好地了解在非二进制环境中选择哪些超参数。基于此类见解，我们提出了CBDM的特定实施，用于估算剂量响应曲线，并通过实验与其他现有方法进行竞争性能进行证明。
In observational studies, weighting methods that directly optimize the balance between treatment and covariates have received much attention lately; however these have mainly focused on binary treatments. Inspired by domain adaptation, we show that such methods can be actually reformulated as specific implementations of a discrepancy minimization problem aimed at tackling a shift of distribution from observational to interventional data. More precisely, we introduce a new framework, Covariate Balance via Discrepancy Minimization (CBDM), that provably encompasses most of the existing balancing weight methods and formally extends them to treatments of arbitrary types (e.g., continuous or multivariate). We establish theoretical guarantees for our framework that both offer generalizations of properties known when the treatment is binary, and give a better grasp on what hyperparameters to choose in non-binary settings. Based on such insights, we propose a particular implementation of CBDM for estimating dose-response curves and demonstrate through experiments its competitive performance relative to other existing approaches for continuous treatments.
