Existing weighting methods for treatment effect estimation are often built upon the idea of propensity scores or covariate balance. They usually impose strong assumptions on treatment assignment or outcome model to obtain unbiased estimation, such as linearity or specific functional forms, which easily leads to the major drawback of model mis-specification. In this paper, we aim to alleviate these issues by developing a distribution learning-based weighting method. We first learn the true underlying distribution of covariates conditioned on treatment assignment, then leverage the ratio of covariates' density in the treatment group to that of the control group as the weight for estimating treatment effects. Specifically, we propose to approximate the distribution of covariates in both treatment and control groups through invertible transformations via change of variables. To demonstrate the superiority, robustness, and generalizability of our method, we conduct extensive experiments using synthetic and real data. From the experiment results, we find that our method for estimating average treatment effect on treated (ATT) with observational data outperforms several cutting-edge weighting-only benchmarking methods, and it maintains its advantage under a doubly-robust estimation framework that combines weighting with some advanced outcome modeling methods.

翻译：现有用于治疗效应估计的加权方法通常基于倾向性得分或协变量平衡的思想。为获得无偏估计，这些方法常对治疗分配或结果模型施加强假设（如线性或特定函数形式），这容易导致模型误设的主要缺陷。本文旨在通过开发一种基于分布学习的加权方法以缓解上述问题。我们首先学习基于治疗分配的协变量真实潜在分布，进而利用治疗组协变量密度与对照组协变量密度之比作为估计治疗效应的权重。具体而言，我们提出通过变量变换的逆可逆映射来近似治疗组和对照组的协变量分布。为证明所提方法的优越性、鲁棒性和泛化能力，我们使用合成数据和真实数据进行了大量实验。实验结果表明，在利用观测数据估计处理组平均治疗效应（ATT）时，我们的方法优于多个前沿的纯加权基准方法，并且在结合加权与先进结果建模方法的双稳健估计框架下仍保持其优势。