@article{rubin2005causal,
  title={Causal inference using potential outcomes: Design, modeling, decisions},
  author={Rubin, Donald B},
  journal={Journal of the American Statistical Association},
  volume={100},
  number={469},
  pages={322--331},
  year={2005},
  publisher={Taylor \& Francis},
  url={https://www.jstor.org/stable/27590541}
}

@article{pearl1995causal,
  title={Causal diagrams for empirical research},
  author={Pearl, Judea},
  journal={Biometrika},
  volume={82},
  number={4},
  pages={669--688},
  year={1995},
  publisher={Oxford University Press},
  url={https://www.jstor.org/stable/2337329}
}

@article{kunzel2019metalearners,
  title={Metalearners for estimating heterogeneous treatment effects using machine learning},
  author={K{\"u}nzel, S{\"o}ren R and Sekhon, Jasjeet S and Bickel, Peter J and Yu, Bin},
  journal={Proceedings of the national academy of sciences},
  volume={116},
  number={10},
  pages={4156--4165},
  year={2019},
  publisher={National Acad Sciences},
  url={https://arxiv.org/abs/1706.03461}
}

@article{fulcher2020robust,
  title={Robust inference on population indirect causal effects: the generalized front door criterion},
  author={Fulcher, Isabel R and Shpitser, Ilya and Marealle, Stella and Tchetgen Tchetgen, Eric J},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={82},
  number={1},
  pages={199--214},
  year={2020},
  publisher={Oxford University Press},
  url={https://academic.oup.com/jrsssb/article/82/1/199/7056027}
}

@article{guo2023targeted,
  title={Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional},
  author={Guo, Anna and Benkeser, David and Nabi, Razieh},
  journal={arXiv preprint arXiv:2312.10234},
  year={2023},
  url={https://arxiv.org/abs/2312.10234}
}

@article{brito2012generalized,
  title={Generalized instrumental variables},
  author={Brito, Carlos and Pearl, Judea},
  journal={arXiv preprint arXiv:1301.0560},
  year={2012},
  url={https://ftp.cs.ucla.edu/pub/stat_ser/r303-reprint.pdf}
}

@inproceedings{van2015efficiently,
  title={Efficiently finding conditional instruments for causal inference},
  author={Van der Zander, Benito and Textor, Johannes and Liskiewicz, Maciej},
  booktitle={Twenty-Fourth International Joint Conference on Artificial Intelligence},
  year={2015},
  url={https://www.ijcai.org/Proceedings/15/Papers/457.pdf}
}

@article{lunceford2004stratification,
  title={Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study},
  author={Lunceford, Jared K and Davidian, Marie},
  journal={Statistics in medicine},
  volume={23},
  number={19},
  pages={2937--2960},
  year={2004},
  publisher={Wiley Online Library},
  url={see https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.1903}
}

@article{choi2021instrumental,
  title={Instrumental variable estimation of truncated local average treatment effects},
  author={Choi, Byeong Yeob},
  journal={Plos one},
  volume={16},
  number={4},
  pages={e0249642},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA},
  url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021190/}
}

@article{angrist1995two,
  title={Two-stage least squares estimation of average causal effects in models with variable treatment intensity},
  author={Angrist, Joshua D and Imbens, Guido W},
  journal={Journal of the American statistical Association},
  volume={90},
  number={430},
  pages={431--442},
  year={1995},
  publisher={Taylor \& Francis},
  url={https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476535}
}

@techreport{wager2020stats,
  title={Stats 361: Causal inference},
  author={Wager, Stefan},
  year={2020},
  institution={Technical report, Technical report, Stanford University, 2020. URL: https~…},
  url={https://web.stanford.edu/~swager/stats361.pdf}
}

@article{neal2020introduction,
  title={Introduction to causal inference: From a machine learning perspective. Course Lect},
  author={Neal, B.},
  journal={Notes},
  year={2020},
  url={https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf}
}

@article{chen2020causalml,
  title={Causalml: Python package for causal machine learning},
  author={Chen, Huigang and Harinen, Totte and Lee, Jeong-Yoon and Yung, Mike and Zhao, Zhenyu},
  journal={arXiv preprint arXiv:2002.11631},
  year={2020},
  url={https://causalml.readthedocs.io/en/latest/}
}

@inproceedings{nodelam2003learningctbn,
  author = {Nodelman, Uri and Shelton, Christian R. and Koller, Daphne},
  title = {Learning continuous time bayesian networks},
  year = {2002},
  isbn = {0127056645},
  publisher = {Morgan Kaufmann Publishers Inc.},
  address = {San Francisco, CA, USA},
  abstract = {Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve nver continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of leaning parameters and structure of a CTBN from fully observed data. We define a conjugate prior for CTBNs and show how it can be used both for Bayesian parameter estimation and as the basis of a Bayesian score for structure learning. Because acyclicity is not a constraint in CTBNs, we can show that the structure leaning problem is significantly easier, both in theory and in practice, than structure leaning for dynamic Bayesian networks (DBNs). Furthermore, as CTBNs can tailor the parameters and dependency structure to the different time granularities of the evolution of different variables, they can provide a better fit to continuous-time processes than DBNs with a fixed time granularity.},
  booktitle = {Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence},
  pages = {451–458},
  numpages = {8},
  location = {Acapulco, Mexico},
  series = {UAI'03}
}


@InProceedings{bregoli20a,
  title = 	 {Constraing-Based Learning for Continous-Time Bayesian Networks},
  author =       {Bregoli, Alessandro and Scutari, Marco and Stella, Fabio},
  booktitle = 	 {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
  pages = 	 {41--52},
  year = 	 {2020},
  editor = 	 {Jaeger, Manfred and Nielsen, Thomas Dyhre},
  volume = 	 {138},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--25 Sep},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v138/bregoli20a/bregoli20a.pdf},
  url = 	 {https://proceedings.mlr.press/v138/bregoli20a.html},
  abstract = 	 {Dynamic Bayesian networks have been well explored in the literature as discrete-time models; however, their continuous-time extensions have seen comparatively little attention.
 In this paper, we propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian networks.
 We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence.
 Finally, we validate its performance using synthetic data, and discuss its strengths and limitations.
 We find that score-based is more accurate in learning networks with binary variables, while our
 constraint-based approach is more accurate with variables assuming more than two values. However,
 more experiments are needed for confirmation.}
}
