Citing dml-rs

If you use dml-rs in your work, please cite the package and the underlying method:

@software{gudzevic_dml_rs,
  author  = {Gudzevic, Asad},
  title   = {dml-rs: Double / debiased machine learning in Rust and Python},
  url     = {https://github.com/AsadGudzevic94/dml-rs},
  year    = {2026}
}

@article{chernozhukov2018double,
  author  = {Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and
             Hansen, Christian and Newey, Whitney and Robins, James},
  title   = {Double/debiased machine learning for treatment and structural parameters},
  journal = {The Econometrics Journal},
  volume  = {21},
  number  = {1},
  pages   = {C1--C68},
  year    = {2018},
  doi     = {10.1111/ectj.12097}
}

GitHub's "Cite this repository" button provides the same information from CITATION.cff.

References

• Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.
• Chernozhukov, V., Cinelli, C., Newey, W., Sharma, A., & Syrgkanis, V. (2022). Long story short: Omitted variable bias in causal machine learning. NBER Working Paper 30302.
• Chiang, H. D., Kato, K., Ma, Y., & Sasaki, Y. (2022). Multiway cluster robust double/debiased machine learning. Journal of Business & Economic Statistics, 40(3), 1046–1056.
• Bach, P., Chernozhukov, V., Kurz, M. S., & Spindler, M. (2022). DoubleML – An object-oriented implementation of double machine learning in Python. Journal of Machine Learning Research, 23(53), 1–6.
• Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230.
• Sant'Anna, P. H. C., & Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics, 219(1), 101–122.
• Semenova, V., & Chernozhukov, V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24(2), 264–289.
• Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. PNAS, 116(10), 4156–4165.
• Nie, X., & Wager, S. (2021). Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2), 299–319.
• Athey, S., & Wager, S. (2021). Policy learning with observational data. Econometrica, 89(1), 133–161.
• Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008–3049.