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

The UAI paper [MGW20] provides basic definitions and first results for MeDIL causal models. .. code-block:: bibtex


author = {Markham, Alex and Grosse-Wentrup, Moritz}, title = {Measurement Dependence Inducing Latent Causal Models}, year = 2020, issn = {2640-3498}, url = {}, journal = {Conference on Uncertainty in Artificial Intelligence (UAI)}, publisher = {PMLR}


The PGM software demonstration [MCGW20] is specifically associated with the MeDIL Python package. .. code-block:: bibtex


author = {Markham, Alex, and Chivukula, Aditya and Grosse-Wentrup, Moritz}, title = {MeDIL: A Python Package for Causal Modelling}, year = 2020, issn = {2640-3498}, journal = {International Conference on Probabilistic Graphical Models (PGM) Software Demonstration}, publisher = {PMLR}


The arXiv preprint [MLAS23] further develops the MeDIL causal model framework, more explicitly connects it to factor analysis, and extends the framework to a deep generative model. .. code-block:: bibtex

author = {Alex Markham and Mingyu Liu and Bryon Aragam and

Liam Solus},

title = {Neuro-Causal Factor Analysis}, year = 2023, note = {preprint, arXiv:2305.19802 [stat.ML]}, url = {}