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References

This page lists every work that the guides cite. Each guide also lists its own citations at the foot of the page. The entries come from references.bib, and apa.csl sets the author-year style of the site and the PDF. On GitHub this page does not render, so read references.bib there.

  1. Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2). https://doi.org/10.1214/18-AOS1709 

  2. 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. https://doi.org/10.1111/ectj.12097 

  3. Dandl, S., Haslinger, C., Hothorn, T., Seibold, H., Sverdrup, E., Wager, S., & Zeileis, A. (2024). What makes forest-based heterogeneous treatment effect estimators work? The Annals of Applied Statistics, 18(1). https://doi.org/10.1214/23-AOAS1799 

  4. Javaloy, A., Sánchez-Martín, P., & Valera, I. (2023). Causal normalizing flows: From theory to practice. Advances in Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/2306.05415 

  5. Khemakhem, I., Monti, R. P., Leech, R., & Hyvärinen, A. (2021). Causal autoregressive flows. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, 130, 3520–3528. https://arxiv.org/abs/2011.02268 

  6. Nie, X., & Wager, S. (2021). Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2), 299–319. https://doi.org/10.1093/biomet/asaa076 

  7. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161 

  8. Robinson, P. M. (1988). Root-N-consistent semiparametric regression. Econometrica, 56(4). https://doi.org/10.2307/1912705 

  9. Sánchez-Martín, P., Rateike, M., & Valera, I. (2022). VACA: Designing variational graph autoencoders for causal queries. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 8159–8168. https://doi.org/10.1609/aaai.v36i7.20789 

  10. Sick, B., & Dürr, O. (2025). Interpretable neural causal models with TRAM-DAGs. Proceedings of the 4th Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research, 275, 606–630. https://arxiv.org/abs/2503.16206 

  11. Zeileis, A., & Hornik, K. (2007). Generalized M-fluctuation tests for parameter instability. Statistica Neerlandica, 61(4), 488–508. https://doi.org/10.1111/j.1467-9574.2007.00371.x 

  12. Zeileis, A., Hothorn, T., & Hornik, K. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514. https://doi.org/10.1198/106186008X319331