Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effects. However, these data suffer from several weaknesses, leading to inaccurate causal effect estimations, if not handled properly. Therefore, several machine learning techniques have been proposed, most of them focusing on leveraging the predictive power of neural network models to attain more precise estimation of causal effects. In this work, we propose a new methodology, named Nearest Neighboring Information for Causal Inference (NNCI), for integrating valuable nearest neighboring information on neural network-based models for estimating treatment effects. The proposed NNCI methodology is applied to some of the most well established neural network-based models for treatment effect estimation with the use of observational data. Numerical experiments and analysis provide empirical and statistical evidence that the integration of NNCI with state-of-the-art neural network models leads to considerably improved treatment effect estimations on a variety of well-known challenging benchmarks.

治疗效果估计对于许多科学和工业领域的研究人员和从业者来说都非常重要。大量的观测数据使得研究人员越来越多地使用它们来估计因果效应。然而，这些数据存在一些弱点，如果处理不当，就会导致因果效应估计不准确。因此，人们提出了几种机器学习技术，其中大多数侧重于利用神经网络模型的预测能力来更精确地估计因果效应。在这项工作中，我们提出了一种名为因果推理最近邻信息（NNCI）的新方法，用于将有价值的最近邻信息集成到基于神经网络的模型上以估计治疗效果。所提出的 NNCI 方法适用于一些最完善的基于神经网络的模型，用于使用观察数据估计治疗效果。数值实验和分析提供了经验和统计证据，表明 NNCI 与最先进的神经网络模型的集成可以显着改善对各种众所周知的具有挑战性的基准的治疗效果估计。