The scarcity of high-dimensional causal inference datasets restricts the exploration of complex deep models. In this work, we propose a method to generate a synthetic causal dataset that is high-dimensional. The synthetic data simulates a causal effect using the MNIST dataset with Bernoulli treatment values. This provides an opportunity to study varieties of models for causal effect estimation. We experiment on this dataset using Dragonnet architecture (Shi et al. (2019)) and modified architectures. We use the modified architectures to explore different types of initial Neural Network layers and observe that the modified architectures perform better in estimations. We observe that residual and transformer models estimate treatment effect very closely without the need for targeted regularization, introduced by Shi et al. (2019).

翻译：高维因果推断数据集的稀缺限制了复杂深度模型的探索。本文提出一种生成高维合成因果数据集的方法。该合成数据利用包含伯努利处理变量的MNIST数据集模拟因果效应，为研究各类因果效应估计模型提供了契机。我们采用Dragonnet架构（Shi等人，2019）及其改进架构对该数据集进行实验。通过改进架构探索不同类型初始神经网络层，观察到改进架构在估计效果上表现更优。同时发现，残差模型与Transformer模型无需Shi等人（2019）提出的目标正则化即可精准估计处理效应。