This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer 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 and PyTorch.

中文翻译：

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因果推理深度学习入门


本入门书系统化了在潜在结果框架下使用深度神经网络进行因果推理的新兴文献。它直观地介绍了构建和优化自定义深度学习模型，并展示了如何调整它们来估计/预测异质治疗效果。它还讨论了正在进行的工作，将因果推理扩展到混杂是非线性的、随时间变化的或以文本、网络和图像编码的环境。为了最大限度地提高可访问性，我们还引入了因果推理和深度学习的先决概念。该入门书与深度学习和因果推理的其他处理方法的不同之处在于，它重点关注观察因果估计，对关键算法进行扩展阐述，以及在 TensorFlow 2 和 PyTorch 中实现、训练和选择深度估计器的详细教程。