Textbooks treatments covering DML

• Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M. & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. https://causalml-book.org/.
• Huber, Martin (2023). Causal analysis: Impact evaluation and Causal Machine Learning with applications in R. MIT Press.
• Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC

Software packages implementing DML

• ddml for Stata by Ahrens, Hansen, Schaffer, Wiemann (2024).
  • Package tutorials
  • Ahrens A, Hansen C B, Schaffer M E, Wiemann T (2024). ddml: Double/debiased machine learning in Stata. Stata Journal. 24(1): 3-45. https://doi.org/10.1177/1536867X2412336
• ddml for R by Wiemann, Ahrens, Hansen, Schaffer (2024).
  • Package vignette
• DoubleML for R and Python.
  • The package website provides extensive tutorials.
  • Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022). DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python. Journal of Machine Learning Research. 23(53): 1-6, https://www.jmlr.org/papers/v23/21-0862.html.
  • Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M. and Klaassen, S. (2024), DoubleML - An Object-Oriented Implementation of Double Machine Learning in R. Journal of Statistical Software. 108(3): 1-56. https://www.jstatsoft.org/article/view/v108i03
• EconML for Python, see module website
• CausalELM for Julia includes a DML program, see website

Other online tutorials

• The online tutorial accompanying Mogstad & Torgovitsky (“Instrumental Variables with Heterogeneous Treatment Effects,” 2024, Handbook of Labor Economics) includes a demonstration of how to use DML for the estimation of Local Average Treatment Effects and Average Causal Responses.

Video lectures

• Victor Chernozhukov, “Double Machine Learning for Causal and Treatment Effects”.

References on supervised machine learning

Textbooks:

• James, G, Witten, Daniela, Hastie, Trevor & Tibshirani, R (2023). An Introduction to Statistical Learning with Applications in R. https://www.statlearning.com/
• Hastie, Trevor, Robert Tibshirani, Jerome H. Friedman, and Jerome H. Friedman. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009. https://hastie.su.domains/ElemStatLearn/

Review papers:

• Melissa Dell, Deep Learning for Economists, https://arxiv.org/abs/2407.15339
• Athey, Susan, and Guido W. Imbens. 2019. Machine Learning Methods That Economists Should Know About. Annual Review of Economics. 11(Volume 11, 2019): 685–725. https://doi.org/10.1146/annurev-economics-080217-053433

Other references

Ahrens, A., Hansen, C.B., Schaffer, M.E. and Wiemann, T. (2025). Model Averaging and Double Machine Learning. Journal of Applied Econometrics. https://doi.org/10.1002/jae.3103

Ahrens A, Hansen C B, Schaffer M E, Wiemann T (2024). ddml: Double/debiased machine learning in Stata. Stata Journal. 24(1), 3-45. https://doi.org/10.1177/1536867X2412336

Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022). DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python. Journal of Machine Learning Research. 23(53), 1-6. https://www.jmlr.org/papers/v23/21-0862.html

Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M. and Klaassen, S. (2024), DoubleML - An Object-Oriented Implementation of Double Machine Learning in R. Journal of Statistical Software. 108(3), 1-56. https://www.jstatsoft.org/article/view/v108i03

Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of econometrics, 225(2), 200-230. https://doi.org/10.1016/j.jeconom.2020.12.001

Dobkin, Carlos, Amy Finkelstein, Raymond Kluender, and Matthew J. Notowidigdo. 2018. The Economic Consequences of Hospital Admissions. American Economic Review 108 (2), 308–52.

Paola Giuliano, Nathan Nunn, Understanding Cultural Persistence and Change, The Review of Economic Studies. 88(4), 1541–1581. https://doi.org/10.1093/restud/rdaa074

Poterba, J. M., Venti, S. F., & Wise, D. A. (1995). Do 401 (k) contributions crowd out other personal saving?. Journal of Public Economics, 58(1), 1-32.

Sant’Anna, P. H., & Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of econometrics, 219(1), 101-122.

Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of econometrics. 225(2), 175-199.