标题： 自平衡神经网络：一种估计平均处理效应的新方法 显示英文标题

标题： Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect

摘要： 在观察性研究中，混杂变量同时影响处理和结果。 此外，工具变量也会影响处理分配机制。 这种情况使研究区别于标准的随机对照试验，因为在随机对照试验中，处理分配是随机的。 由于这种情况，估计的平均处理效应会存在偏差。 为了解决这个问题，一种标准方法是在估计平均处理效应时结合估计的倾向得分。 然而，这些方法在倾向得分模型中存在误设的风险。 为了解决这个问题，本研究提出了一种称为“自平衡神经网络”（Sbnet）的新方法，该方法让模型自身从平衡网络中获得其伪倾向得分。 所提出的方法通过将平衡网络作为前馈神经网络的关键部分来估计平均处理效应。 这种公式在一个步骤中解决了平均处理效应的估计问题。 此外，还提出了一个多伪倾向得分框架，该框架从多样化的平衡网络中估计，并用于平均处理效应的估计。 最后，所提出的方法在三个模拟设置和真实世界数据集上与最先进的方法进行了比较。 结果表明，所提出的自平衡神经网络在性能上优于最先进的方法。 显示英文摘要

摘要： In observational studies, confounding variables affect both treatment and outcome. Moreover, instrumental variables also influence the treatment assignment mechanism. This situation sets the study apart from a standard randomized controlled trial, where the treatment assignment is random. Due to this situation, the estimated average treatment effect becomes biased. To address this issue, a standard approach is to incorporate the estimated propensity score when estimating the average treatment effect. However, these methods incur the risk of misspecification in propensity score models. To solve this issue, a novel method called the "Self balancing neural network" (Sbnet), which lets the model itself obtain its pseudo propensity score from the balancing net, is proposed in this study. The proposed method estimates the average treatment effect by using the balancing net as a key part of the feedforward neural network. This formulation resolves the estimation of the average treatment effect in one step. Moreover, the multi-pseudo propensity score framework, which is estimated from the diversified balancing net and used for the estimation of the average treatment effect, is presented. Finally, the proposed methods are compared with state-of-the-art methods on three simulation setups and real-world datasets. It has been shown that the proposed self-balancing neural network shows better performance than state-of-the-art methods.