Deep Learning of Potential Outcomes

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...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Koch, Bernard, Sainburg, Tim, Geraldo, Pablo, Jiang, Song, Sun, Yizhou, Jacob Gates Foster
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Deep learning
Inference
Literature reviews
Machine learning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422