[64] Robins, J. M., Hernan, M. A. and Brumback, B. [2000], 'Marginal structural models and causal inference in epidemiology', Epidemiology pp. 550-560.
[65] Rolland, P., Cevher, V., Kleindessner, M., Russell, C., Janzing, D., Schölkopf, B. and Locatello, F. [2022], Score matching enables causal discovery of nonlinear additive noise models, in 'International Conference on Machine Learning', PMLR, pp. 18741-18753.
[66] Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A. and Nolan, G. P. [2005], 'Causal protein-signaling networks derived from multiparameter single-cell data', Science 308(5721), 523-529.
[67] Saeed, B., Panigrahi, S. and Uhler, C. [2020], Causal structure discovery from distributions arising from mixtures of dags, in 'International Conference on Machine Learning', PMLR, pp. 8336-8345.
[68] Sanchez, P., Liu, X., O'Neil, A. Q. and Tsaftaris, S. A. [2022], 'Diffusion models for causal discovery via topological ordering', arXiv preprint arXiv:2210.06201.
[69] Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A. and Bengio, Y. [2021], 'Toward causal representation learning', Proceedings of the IEEE 109(5), 612-634.
[70] Shimizu, S., Hoyer, P. O., Hyvarinen, A., Kerminen, A. and Jordan, M. [2006], 'A linear non-gaussian acyclic model for causal discovery', Journal of Machine Learning Research.
[71] Spirtes, P., Glymour, C. N., Scheines, R. and Heckerman, D. [2000], Causation, prediction, and search, MIT press.
[72] Squires, C., Wang, Y. and Uhler, C. [2020], Permutation-based causal structure learning with unknown intervention targets, in 'Conference on Uncertainty in Artificial Intelligence', PMLR, pp. 1039-1048.
[73] Stein, C. [1972], A bound for the error in the normal approximation to the distribution of a sum of dependent random variables, in 'Proc. Sixth Berkeley Symp. Math. Stat. Prob.', pp. 583-602.
[74] Tanay, A., Regev, A. and Shamir, R. [2005], 'Conservation and evolvability in regulatory networks: the evolution of ribosomal regulation in yeast', Proceedings of the National Academy of Sciences 102(20), 7203-7208.
[75] Teyssier, M. and Koller, D. [2012], 'Ordering-based search: A simple and effective algorithm for learning bayesian networks', arXiv preprint arXiv:1207.1429.
[76] Tsamardinos, I., Brown, L. E. and Aliferis, C. F. [2006], 'The max-min hill-climbing Bayesian network structure learning algorithm', Machine Learning 65(1), 31-78.
[77] Uhler, C., Raskutti, G., Buhlmann, P. and Yu, B. [2013], 'Geometry of the faithfulness assumption in causal inference', The Annals of Statistics pp. 436-463.
[78] Van de Geer, S. and Buhlmann, P. [2013], ‘ $\ell_0$ -penalized maximum likelihood for sparse directed acyclic graphs’, The Annals of Statistics 41(2), 536–567.
[79] Varici, B., Shanmugam, K., Sattigeri, P. and Tajer, A. [2021], 'Scalable intervention target estimation in linear models', Advances in Neural Information Processing Systems 34, 1494-1505.
[80] Voorman, A., Shojaie, A. and Witten, D. [2014], 'Graph estimation with joint additive models', Biometrika 101(1), 85-101.
[81] Vucic, D. and Fairbrother, W. J. [2007], 'The inhibitor of apoptosis proteins as therapeutic targets in cancer', *Clinical cancer research* 13(20), 5995-6000.
[82] Wang, Y., Squires, C., Belyaeva, A. and Uhler, C. [2018], 'Direct estimation of differences in causal graphs', Advances in neural information processing systems 31.
[83] Yang, K., Katcoff, A. and Uhler, C. [2018], Characterizing and learning equivalence classes of causal dags under interventions, in 'International Conference on Machine Learning', PMLR, pp. 5541-5550.
[84] Yoon, H., Jang, H., Kim, E.-Y., Moon, S., Lee, S., Cho, M., Cho, H. J., Ko, J. J., Chang, E. M., Lee, K.-A. et al. [2018], 'Knockdown of prkar2b results in the failure of oocyte maturation', Cellular Physiology and Biochemistry 45(5), 2009–2020.
[85] Yuan, H., Xi, R., Chen, C. and Deng, M. [2017], 'Differential network analysis via lasso penalized D-trace loss', Biometrika.