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Applied Causal Inference Powered by ML and AI

Author

Listed:
  • Victor Chernozhukov
  • Christian Hansen
  • Nathan Kallus
  • Martin Spindler
  • Vasilis Syrgkanis

Abstract

An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.

Suggested Citation

  • Victor Chernozhukov & Christian Hansen & Nathan Kallus & Martin Spindler & Vasilis Syrgkanis, 2024. "Applied Causal Inference Powered by ML and AI," Papers 2403.02467, arXiv.org.
    • Handle: RePEc:arx:papers:2403.02467
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    File URL: http://arxiv.org/pdf/2403.02467
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    References listed on IDEAS

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    1. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Rocío Titiunik, 2019. "Regression Discontinuity Designs Using Covariates," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 442-451, July.
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    Cited by:

    1. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.

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