G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. An analysis of approximations for maximizing submodular set functions—i. Mathematical Programming, 14(1):265–294, Dec 1978. ISSN 1436-4646. doi: 10.1007/BF01588971. URL https://doi.org/10.1007/BF01588971. (Cited on page 8)
Judea Pearl. Causality. Cambridge University Press, Cambridge, UK, 2 edition, 2009. ISBN 978-0-521-89560-6. doi: 10.1017/CBO9780511803161. (Cited on page 1)
Jonas Peters, Peter Buhlmann, and Nicolai Meinshausen. Causal inference by using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(5):947-1012, 2016. (Cited on page 1)
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017. (Cited on page 1, 3)
Karen Sachs, Omar Perez, Dana Pe'er, Douglas A Lauffenburger, and Garry P Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523-529, 2005. (Cited on page 1)
Ruben Sanchez-Romero, Joseph D Ramsey, Kun Zhang, Madelyn RK Glymour, Biwei Huang, and Clark Glymour. Estimating feedforward and feedback effective connections from fmri time series: Assessments of statistical methods. Network Neuroscience, 3(2):274-306, 2019. (Cited on page 1)
Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C Mozer, Yoshua Bengio, Stefan Bauer, and Nan Rosemary Ke. Learning neural causal models with active interventions. arXiv preprint arXiv:2109.02429, 2021. (Cited on page 2, 4, 6, 7, 21)
Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, and Nan Rosemary Ke. On the generalization and adaption performance of causal models. arXiv preprint arXiv:2206.04620, 2022. (Cited on page 1)
Marco Scutari. Learning bayesian networks with the bnlearn r package. Journal of Statistical Software, 35(3):1-22, 2010. doi: 10.18637/jss.v035.i03. URL https://www.jstatsoft.org/index.php/jss/article/view/v035i03. (Cited on page 7)
Burr Settles, Mark Craven, and Soumya Ray. Multiple-instance active learning. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems, volume 20. Curran Associates, Inc., 2007. URL https://proceedings.neurips.cc/paper/2007/file/a1519de5b5d44b31a01de013b9b51a80-Paper.pdf. (Cited on page 2, 3)
Xinpeng Shen, Sisi Ma, Prashanthi Vemuri, and Gyorgy Simon. Challenges and opportunities with causal discovery algorithms: application to alzheimer's pathophysiology. *Scientific reports*, 10(1): 1-12, 2020. (Cited on page 1)
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT press, 2nd edition, 2000a. (Cited on page 1)
Peter Spirtes, Clark N Glymour, Richard Scheines, David Heckerman, Christopher Meek, Gregory Cooper, and Thomas Richardson. Causation, prediction, and search. MIT press, 2000b. (Cited on page 1, 3)
Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, and Karthikeyan Shanmugam. Active structure learning of causal dags via directed clique trees. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/bit/57bd0a58e953e5c43cd4a4e5af46138-AAbstract.html. (Cited on page 2)
Panagiotis Tugas, Yashas Annadani, Andrew Jesson, Bernhard Scholkopf, Yarin Gal, and Stefan Bauer. Interventions, where and how? experimental design for causal models at scale, 2022. URL https://arxiv.org/abs/2203.02016. (Cited on page 2, 4, 8, 20, 21)