This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.

翻译：本综述系统梳理了基于潜在结果框架运用深度神经网络进行因果推断的新兴文献。我们直观阐释了如何利用深度学习估计/预测异质性处理效应，并将因果推断拓展至存在非线性混淆、时变混淆或混淆信息编码于文本、网络及图像中的场景。为提升可读性，本文还介绍了因果推断与深度学习的基础概念。与同类深度学习因果推断研究相比，本综述聚焦于观测性因果估计、对核心算法的深入阐释，以及基于TensorFlow 2实现、训练与筛选深度估计器的详细教程（代码详见github.com/kochbj/Deep-Learning-for-Causal-Inference）。