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Deep Learning of Potential Outcomes

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  • Koch, Bernard
  • Sainburg, Tim
  • Geraldo, Pablo
  • JIANG, SONG
  • Sun, Yizhou
  • Foster, Jacob G.

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.

Suggested Citation

  • Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
    • Handle: RePEc:osf:socarx:aeszf
    DOI: 10.31219/osf.io/aeszf
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    References listed on IDEAS

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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Yixin Wang & David M. Blei, 2019. "The Blessings of Multiple Causes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1574-1596, October.
    3. Yixin Wang & David M. Blei, 2019. "The Blessings of Multiple Causes: Rejoinder," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1616-1619, October.
    4. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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