Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes for leveraging this data. In this work, we propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture. The proposed model exploits the covariates as well as the outcomes of neighboring instances in training data. Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.

翻译：目前,在许多科学和工业领域,越来越需要估计治疗效果和回答因果问题,解决这些问题的关键是大量的观测数据和利用这些数据的过程。在这项工作中,我们提出了一个预测潜在结果和倾向性分数的新模型,该模型以神经网络结构为基础。拟议模型利用培训数据中的共差和相邻实例的结果。数字实验表明,拟议的模型报告与最新模型相比,更好的处理效果估计。