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：这项审查利用潜在结果框架下的深神经网络,将新的因果关系推断文献系统化,利用潜在结果框架下的深神经网络,对如何利用深度学习来估计/预测不同处理效果,并将因果关系推断扩大到非线性、时间差异或文字、网络和图像编码的环境下;为了尽量扩大可获取性,我们还引入了因果推断和深学习的前提条件概念;这项调查与其他对深度学习和因果推断的处理方法不同,其侧重点是观察性因果关系估计、对关键算法的扩展阐述,以及用于实施、培训和选择Tensorflow 2的深度估计者的详细辅导,见github.com/kochbj/Deep-Learch-forCausal-Inference。