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:治疗效果估计的现有加权方法往往以偏好分数或共差平衡概念为基础,通常对治疗分配或结果模型进行严格的假设,以获得公正的估计,例如线性或特定功能形式,这很容易导致模型偏差的重大缺陷。在本文中,我们的目标是通过开发基于分配的基于学习的加权法来缓解这些问题。我们首先了解以治疗分配为条件的共变体的真正基本分布,然后将治疗组群中的共变密度与控制组群中的共变密度之比作为估计治疗效果的权重。具体地说,我们提议通过变异变异的不可逆的变化来估计治疗和控制组群中的共变的分布。为了表明我们方法的优越性、稳健性和可概括性,我们利用合成和真实的数据进行了广泛的实验。我们从实验结果中发现,我们估算治疗(ATT)的平均治疗效果的方法比观察数据比一些尖端的衡量基准方法要好,并在一个将加权和一些先进结果模型相结合的双重紫色估计框架下保持其优势。