论文标题
改进的神经网络模型，用于治疗效果估计
An improved neural network model for treatment effect estimation
论文作者
论文摘要
如今，在许多科学和工业领域中，越来越需要估计治疗效果并回答因果问题。解决这些问题的关键是大量观察数据和利用此数据的过程。在这项工作中，我们提出了一个新的模型，以预测基于神经网络体系结构的潜在结果和倾向得分。提出的模型利用了协变量以及培训数据中相邻实例的结果。数值实验表明，与最先进的模型相比，提出的模型报告了更好的治疗效果估计性能。
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.
