Skip to content

Citation

If you use this library in your research, please cite the original paper:

Ferdous, M. H., Hasan, U., & Gani, M. O. (2023). CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data. In Proceedings of the 8th Machine Learning for Healthcare Conference (PMLR Vol. 219). Paper

BibTeX

@inproceedings{ferdous2023cdans,
  title     = {{CDANs}: Temporal Causal Discovery from Autocorrelated and
               Non-Stationary Time Series Data},
  author    = {Ferdous, Muhammad Hasan and Hasan, Uzma and Gani, Md Osman},
  booktitle = {Proceedings of the 8th Machine Learning for Healthcare Conference},
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  volume    = {219},
  year      = {2023},
  url       = {https://proceedings.mlr.press/v219/ferdous23a.html}
}

Citing this implementation specifically

A CITATION.cff file is shipped at the repo root for tools that read it (e.g., GitHub's "Cite this repository" widget). The library itself is MIT-licensed; see LICENSE in the repo.

The CDANs algorithm builds on:

  • PCMCI for lagged-adjacency discovery — Runge et al. (2019), Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, 5(11).
  • CD-NOD for non-stationary causal discovery — Huang, Zhang, Zhang, Ramsey, Sanchez-Romero, Glymour, Schölkopf (2020), Causal Discovery from Heterogeneous/Nonstationary Data. JMLR 21.
  • The independent-change principle — Huang, Zhang, Sanchez-Romero, Ramsey, Glymour, Glymour (2017), Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.
  • PC-stable for order-independent skeleton selection — Colombo & Maathuis (2014), Order-Independent Constraint-Based Causal Structure Learning. JMLR 15.
  • KCI as a kernel-based CI test — Zhang, Peters, Janzing, Schölkopf (2011), Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI.
  • Meek's rules for orientation propagation — Meek (1995), Causal inference and causal explanation with background knowledge. UAI.