Bibliography

  1. Joshua D Angrist and Guido W Imbens. Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American statistical Association, 90(430):431–442, 1995. URL: https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476535.

  2. Sima E. Borujeni, Saideep Nannapaneni, Nam H. Nguyen, Elizabeth C. Behrman, and James E. Steck. Quantum circuit representation of Bayesian networks. Expert Systems with Applications, 176:114768, 2021. URL: https://arxiv.org/abs/2004.14803.

  3. Alessandro Bregoli, Marco Scutari, and Fabio Stella. Constraing-based learning for continous-time bayesian networks. In Manfred Jaeger and Thomas Dyhre Nielsen, editors, Proceedings of the 10th International Conference on Probabilistic Graphical Models, volume 138 of Proceedings of Machine Learning Research, 41–52. PMLR, 23–25 Sep 2020. URL: https://proceedings.mlr.press/v138/bregoli20a.html.

  4. Carlos Brito and Judea Pearl. Generalized instrumental variables. arXiv preprint arXiv:1301.0560, 2012. URL: https://ftp.cs.ucla.edu/pub/stat_ser/r303-reprint.pdf.

  5. Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. Causalml: python package for causal machine learning. arXiv preprint arXiv:2002.11631, 2020. URL: https://causalml.readthedocs.io/en/latest/.

  6. Byeong Yeob Choi. Instrumental variable estimation of truncated local average treatment effects. Plos one, 16(4):e0249642, 2021. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021190/.

  7. Diego Colombo and Marloes H. Maathuis. Order-independent constraint-based causal structure learning. Journal of Machine Learning Research, 15(1):3741–3782, 2014. URL: https://jmlr.org/papers/v15/colombo14a.html.

  8. Isabel R Fulcher, Ilya Shpitser, Stella Marealle, and Eric J Tchetgen Tchetgen. Robust inference on population indirect causal effects: the generalized front door criterion. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(1):199–214, 2020. URL: https://academic.oup.com/jrsssb/article/82/1/199/7056027.

  9. Anna Guo, David Benkeser, and Razieh Nabi. Targeted machine learning for average causal effect estimation using the front-door functional. arXiv preprint arXiv:2312.10234, 2023. URL: https://arxiv.org/abs/2312.10234.

  10. Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, and Tom Claassen. Causal Shapley values: exploiting causal knowledge to explain individual predictions of complex models. In Advances in Neural Information Processing Systems, volume 33, 4778–4789. Curran Associates, Inc., 2020. URL: https://arxiv.org/abs/2011.01625.

  11. Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences, 116(10):4156–4165, 2019. URL: https://arxiv.org/abs/1706.03461.

  12. Guang Hao Low, Theodore J. Yoder, and Isaac L. Chuang. Quantum inference on Bayesian networks. Physical Review A, 89(6):062315, 2014. URL: https://arxiv.org/abs/1402.7359.

  13. Jared K Lunceford and Marie Davidian. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in medicine, 23(19):2937–2960, 2004. URL: see https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.1903.

  14. B. Neal. Introduction to causal inference: from a machine learning perspective. course lect. Notes, 2020. URL: https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf.

  15. Uri Nodelman, Christian R. Shelton, and Daphne Koller. Learning continuous time bayesian networks. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, UAI'03, 451–458. San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc.

  16. Judea Pearl. Causal diagrams for empirical research. Biometrika, 82(4):669–688, 1995. URL: https://www.jstor.org/stable/2337329.

  17. Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, 2018. ISBN 978-0-465-09760-9.

  18. Donald B Rubin. Causal inference using potential outcomes: design, modeling, decisions. Journal of the American Statistical Association, 100(469):322–331, 2005. URL: https://www.jstor.org/stable/27590541.

  19. Dario Simionato and Fabio Vandin. Bounding the family-wise error rate in local causal discovery using Rademacher averages. arXiv preprint arXiv:2212.03742, 2022. URL: https://arxiv.org/abs/2212.03742.

  20. Benito Van der Zander, Johannes Textor, and Maciej Liskiewicz. Efficiently finding conditional instruments for causal inference. In Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015. URL: https://www.ijcai.org/Proceedings/15/Papers/457.pdf.

  21. Stefan Wager. Stats 361: causal inference. Technical Report, Technical report, Technical report, Stanford University, 2020. URL: https …, 2020. URL: https://web.stanford.edu/~swager/stats361.pdf.