英文标题：
《Weighting-Based Treatment Effect Estimation via Distribution Learning》
---
作者：
Dongcheng Zhang, Kunpeng Zhang
---
最新提交年份：
2021
---
英文摘要：
  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.
---
中文摘要：
现有的治疗效果评估加权方法通常建立在倾向评分或协变量平衡的基础上。他们通常对治疗分配或结果模型施加强有力的假设，以获得无偏估计，例如线性或特定的函数形式，这很容易导致模型规格错误的主要缺点。在本文中，我们旨在通过开发一种基于分布学习的加权方法来缓解这些问题。我们首先了解以治疗分配为条件的协变量的真实潜在分布，然后利用治疗组的协变量密度与对照组的协变量密度之比作为评估治疗效果的权重。具体来说，我们建议通过变量变化的可逆变换来近似治疗组和对照组中的协变量分布。为了证明我们方法的优越性、鲁棒性和通用性，我们使用合成和真实数据进行了大量实验。从实验结果中，我们发现我们用观测数据估计治疗平均疗效（ATT）的方法优于几种前沿的仅加权基准方法，并且在结合加权和一些先进的结果建模方法的双稳健估计框架下保持了其优势。
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
PDF下载：
-->