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：处理效应估计对于众多科学和工业领域的研究者及实践者都至关重要。观察性数据的丰富性使其越来越多地被研究人员用于因果效应估计。然而，这些数据存在偏差和若干缺陷，若未妥善处理，会导致因果效应估计不准确。因此，人们提出了多种机器学习技术，其中大多数侧重于利用神经网络模型的预测能力，以实现更精确的因果效应估计。本研究提出了一种名为“基于最近邻信息的因果推断”（NNCI）的新方法，用于将宝贵的最近邻信息整合到基于神经网络的模型中，以估计处理效应。所提出的NNCI方法被应用于一些最成熟的基于神经网络的处理效应估计模型，并结合观察性数据进行使用。数值实验与分析提供了实证和统计证据，表明将NNCI与最先进的神经网络模型整合，能在多个公认的具有挑战性的基准测试中显著改善处理效应估计。