论文标题
小型高维数据集的因果推断
Causal Inference from Small High-dimensional Datasets
论文作者
论文摘要
已经提出了许多方法来通过观察数据估算治疗效果。通常，该方法的选择考虑了应用程序的特征，例如治疗和结果的类型，混杂效果以及数据的复杂性。这些方法隐含地假设样本量足够大，可以训练此类模型，尤其是基于神经网络的估计器。如果不是这种情况怎么办？在这项工作中，我们提出了因果养猪，这是一种在同一特征空间中另一个高维数据集的情况下，在小型高维数据集中估算治疗效果的方法。我们采用一种将学习技术转移到因果推论中的方法。我们的实验表明，这种方法有助于为基于神经网络的方法带来稳定性，并改善小型高维数据集中的治疗效果估计。
Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the neural network-based estimators. What if this is not the case? In this work, we propose Causal-Batle, a methodology to estimate treatment effects in small high-dimensional datasets in the presence of another high-dimensional dataset in the same feature space. We adopt an approach that brings transfer learning techniques into causal inference. Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.
