论文标题
调整神经网络以升高模型
Adapting Neural Networks for Uplift Models
论文作者
论文摘要
提升是个体治疗效应建模的特殊情况。这样的模型涉及特定因素的因果推断，例如营销干预。实际上，这些模型建立在购买产品或服务以改善产品营销的客户数据上。使用i）有条件的平均回归或ii）转化结果回归。大多数现有的方法是改编的分类和回归树的改编。但是，实际上，这些常规方法容易过度拟合。在这里，我们提出了一种使用神经网络的新方法。该表示允许共同优化条件均值和转化结果损失的差异。结果，该模型不仅估计了隆升，而且还确保了预测结果的一致性。我们专注于完全随机的实验，这就是我们数据的情况。我们显示我们提出的方法改善了合成和真实数据的最新方法。
Uplift is a particular case of individual treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention. In practice, these models are built on customer data who purchased products or services to improve product marketing. Uplift is estimated using either i) conditional mean regression or ii) transformed outcome regression. Most existing approaches are adaptations of classification and regression trees for the uplift case. However, in practice, these conventional approaches are prone to overfitting. Here we propose a new method using neural networks. This representation allows to jointly optimize the difference in conditional means and the transformed outcome losses. As a consequence, the model not only estimates the uplift, but also ensures consistency in predicting the outcome. We focus on fully randomized experiments, which is the case of our data. We show our proposed method improves the state-of-the-art on synthetic and real data.
