A Primer on Deep Learning for Causal Inference
It provides an accessible primer for researchers and practitioners in machine learning and statistics to apply deep learning methods to observational causal estimation problems.
This review systematizes the literature on using deep neural networks for causal inference under the potential outcomes framework, focusing on estimating heterogeneous treatment effects and handling non-linear, time-varying, or complex data like text and images.
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.