Hidden confounders discovery methods

[sataset]

Analyze methods like tsFCI to investigate the methodology behind hidden confounders search.

Liubov:

Here we can integrate some of the tricks from tiMINO methods for example

• α (c, e) [9]
• CGC [10]
• PCMCI [8]
• ANLTSM [20]
• tsFCI [21]
• TiMINo [22]
• VAR-LiNGAM [13]
• SDI [23]
• PSTE [11]

Text references

8. Runge, J.; Sejdinovic, D.; Flaxman, S. Detecting causal associations in large nonlinear time series datasets. arXiv 2017, arXiv:1702.07007.
9. Huang, Y.; Kleinberg, S. Fast and Accurate Causal Inference from Time Series Data. In Proceedings of the FLAIRS Conference, Hollywood, FL, USA, 18–20 May 2015; pp. 49–54.
10. Hu, M.; Liang, H. A copula approach to assessing Granger causality. NeuroImage 2014, 100, 125–134.
11. Papana, A.; Kyrtsou, C.; Kugiumtzis, D.; Diks, C. Detecting causality in non-stationary time series using partial symbolic transfer entropy: Evidence in financial data. Comput. Econ. 2016, 47, 341–365.
13. Hyvärinen, A.; Shimizu, S.; Hoyer, P.O. Causal modelling combining instantaneous and lagged effects: An identifiable model based on non-Gaussianity. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 5–9 July 2008; pp. 424–431.
20. Chu, T.; Glymour, C. Search for additive nonlinear time series causal models. J. Mach. Learn. Res. 2008, 9, 967–991.
21. Entner, D.; Hoyer, P.O. On causal discovery from time series data using FCI. In Proceedings of the Fifth European Workshop on Probabilistic Graphical Models, Helsinki, Finland, 13–15 September 2010; pp. 121–128.
22. Peters, J.; Janzing, D.; Schölkopf, B. Causal inference on time series using restricted structural equation models. In Advances in Neural Information Processing Systems; The MIT Press: Cambridge, MA, USA, 2013; pp. 154–162.
23. Jiao, J.; Permuter, H.H.; Zhao, L.; Kim, Y.H.; Weissman, T. Universal estimation of directed information. IEEE Trans. Inf. Theory 2013, 59, 6220–6242.