Unsuitability of NOTEARS for Causal Graph Discovery when Dealing with Dimensional Quantities

Author:

Kaiser MarcusORCID,Sipos Maksim

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Reference19 articles.

1. Bhattacharya R, Nagarajan T, Malinsky D, Shpitser I (2021) Differentiable causal discovery under unmeasured confounding. In: International conference on artificial intelligence and statistics, PMLR, pp 2314–2322

2. Fortin M, Glowinski R (2000) Augmented Lagrangian methods: applications to the numerical solution of boundary-value problems. Elsevier, Amsterdam

3. Glymour C, Zhang K, Spirtes P (2019) Review of causal discovery methods based on graphical models. Front Genet 10:524

4. Kyono T, Zhang Y, van der Schaar M (2020) Castle: regularization via auxiliary causal graph discovery. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in Neural Information Processing Systems, vol 33. Curran Associates Inc, pp 1501–1512

5. Lawrence AR, Kaiser M, Sampaio R, Sipos M (2020) Data generating process to evaluate causal discovery techniques for time series data. Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems

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