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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

    as
    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. Martin Huber, 2024. "An introduction to causal discovery," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-16, December.
    2. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    3. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2024.
    4. Hiroyuki Egami & Md. Shafiur Rahman & Tsuyoshi Yamamoto & Chihiro Egami & Takahisa Wakabayashi, 2024. "Causal effect of video gaming on mental well-being in Japan 2020–2022," Nature Human Behaviour, Nature, vol. 8(10), pages 1943-1956, October.
    5. Aur'elien Bibaut & Nathan Kallus, 2024. "Demistifying Inference after Adaptive Experiments," Papers 2405.01281, arXiv.org.

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