A Primer on Deep Learning for Causal Inference

Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun and Jacob Gates Foster

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Abstract: 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.

Date: 2021-10, Revised 2023-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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