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 biases, 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.

翻译：治疗效果估计对于众多科学和工业领域的研究人员及从业者均具有高度重要性。观测数据的丰富性使其越来越多地被研究者用于因果效应估计。然而，若未经妥善处理，这类数据存在的多种偏差和缺陷会导致因果效应估计不准确。为此，研究者提出了多种机器学习技术，其中大多数致力于利用神经网络模型的预测能力来实现更精确的因果效应估计。本研究提出一种名为"因果推断最近邻信息集成"（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治疗效果估计模型中。所提出的NNCI方法被应用于多个使用观测数据的最成熟神经网络治疗效果估计模型。数值实验与分析提供了经验与统计证据，表明将NNCI与最先进的神经网络模型集成，能够在多个公认的具有挑战性的基准测试中显著提升治疗效果估计的准确性。