Unsuitability of NOTEARS for Causal Graph Discovery

ACM Transactions on Mathematical Software (TOMS) 23

Zhu, C., Byrd, R.H., Lu, P., Nocedal, J.: Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization · 1997

Earlier work this paper cites.

Elsevier (2000)

Fortin, M., Glowinski, R.: Augmented Lagrangian methods: applications to the numerical solution of boundary-value problems · 2000

Earlier work this paper cites.

The Journal of Machine Learning Research 12

Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P.O., Bollen, K.: Directlingam: A direct method for learning a linear non-gaussian structural equation model · 2011

Earlier work this paper cites.

arXiv preprint arXiv:1210.4852 (2012)

Original

Pearl, J.: The do-calculus revisited · 2012

Earlier work this paper cites.

The Journal of Machine Learning Research 15

Loh, P.L., Bühlmann, P.: High-dimensional learning of linear causal networks via inverse covariance estimation · 2014

Earlier work this paper cites.

The MIT Press (2017)

Peters, J., Janzing, D., Schölkopf, B.: Elements of causal inference: foundations and learning algorithms · 2017

Earlier work this paper cites.

Advances in Neural Information Processing Systems (NeurIPS 2018) (2018)

Zheng, X., Aragam, B., Ravikumar, P., Xing, E.P.: Dags with no tears: Continuous optimization for structure learning · 2018

Earlier work this paper cites.

Frontiers in genetics 10

Glymour, C., Zhang, K., Spirtes, P.: Review of causal discovery methods based on graphical models · 2019

Cited alongside, same era.

arXiv preprint arXiv:1910.08527 (2019)

Original

Ng, I., Fang, Z., Zhu, S., Chen, Z., Wang, J.: Masked gradient-based causal structure learning · 2019

Cited alongside, same era.

arXiv preprint arXiv:1911.10500 (2019)

Original

Schölkopf, B.: Causality for machine learning · 2019

Cited alongside, same era.

In: International Conference on Machine Learning, pp. 7154–7163. PMLR (2019)

Yu, Y., Chen, J., Gao, T., Yu, M.: Dag-gnn: Dag structure learning with graph neural networks · 2019

Cited alongside, same era.

In: H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, H. Lin (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1501–1512. Curran Associates, Inc. (2020)

Kyono, T., Zhang, Y., van der Schaar, M.: Castle: Regularization via auxiliary causal graph discovery · 2020

arXiv preprint arXiv:2006.10201 (2020)

Original

Ng, I., Ghassami, A., Zhang, K.: On the role of sparsity and dag constraints for learning linear dags · 2020

Later among the works it cites.

In: International Conference on Artificial Intelligence and Statistics, pp. 1595–1605. PMLR (2020)

Pamfil, R., Sriwattanaworachai, N., Desai, S., Pilgerstorfer, P., Georgatzis, K., Beaumont, P., Aragam, B.: Dynotears: Structure learning from time-series data · 2020

Later among the works it cites.

In: International Conference on Artificial Intelligence and Statistics, pp. 3414–3425. PMLR (2020)

Zheng, X., Dan, C., Aragam, B., Ravikumar, P., Xing, E.: Learning sparse nonparametric dags · 2020

Later among the works it cites.

In: International Conference on Artificial Intelligence and Statistics, pp. 2314–2322. PMLR (2021)

Bhattacharya, R., Nagarajan, T., Malinsky, D., Shpitser, I.: Differentiable causal discovery under unmeasured confounding · 2021

Closest in time.

arXiv preprint arXiv:2102.13647 (2021)