MeDIL: A Python Package for Causal Modelling
Abstract
We present the \texttt{MeDIL} Python package for causal modelling. Its current features focus on (i) non-linear unconditional pairwise independence testing, (ii) constraint-based causal structure learning, and (iii) learning the corresponding functional causal models (FCMs), all for the class of measurement dependence inducing latent (MeDIL) causal models. MeDIL causal models and therefore the \texttt{MeDIL} software package are especially suited for analyzing data from fields such as psychometric, epidemiology, etc. that rely on questionnaire or survey data.
Cite
Text
Markham et al. "MeDIL: A Python Package for Causal Modelling." Proceedings of pgm 2020, 2020.Markdown
[Markham et al. "MeDIL: A Python Package for Causal Modelling." Proceedings of pgm 2020, 2020.](https://mlanthology.org/pgm/2020/markham2020pgm-medil/)BibTeX
@inproceedings{markham2020pgm-medil,
title = {{MeDIL: A Python Package for Causal Modelling}},
author = {Markham, Alex and Chivukula, Aditya and Grosse-Wentrup, Moritz},
booktitle = {Proceedings of pgm 2020},
year = {2020},
pages = {621-624},
volume = {138},
url = {https://mlanthology.org/pgm/2020/markham2020pgm-medil/}
}