Citing

If you make use of the MeDIL package or associated theory, please cite the relevant subset of the following papers:

The UAI paper [MGW20] introduces MeDIL causal models, the strong causal insufficiency assumption, and the reduction of structure learning to minimum edge clique cover.

@InProceedings{Markham_2020_UAI,
  title     = {Measurement Dependence Inducing Latent Causal Models},
  author    = {Markham, Alex and Grosse-Wentrup, Moritz},
  booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)},
  pages     = {590--599},
  year      = 2020,
  volume    = 124,
  publisher = {PMLR},
  url       = {http://proceedings.mlr.press/v124/markham20a.html}
}

The PGM software demonstration [MCGW20] is specifically associated with the MeDIL Python package.

@InProceedings{Markham_2020_PGM,
  title     = {{MeDIL}: {A} {Python} Package for Causal Modelling},
  author    = {Markham, Alex and Chivukula, Aditya and Grosse-Wentrup, Moritz},
  booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM)},
  pages     = {621--624},
  year      = 2020,
  volume    = 138,
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  url       = {https://proceedings.mlr.press/v138/markham20a.html}
}

The NCFA paper [MLAS26] further develops the framework, connects it to factor analysis, introduces identifiability results, and extends MeDIL to deep generative causal mechanisms.

@InProceedings{markham2023neuro,
  title     = {Neuro-Causal Factor Analysis},
  author    = {Markham, Alex and Liu, Mingyu and Aragam, Bryon and Solus, Liam},
  booktitle = {Proceedings of the 13th International Conference on Probabilistic Graphical Models (PGM)},
  year      = 2026,
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR}
}
% TODO: fill in volume, pages, and url once available