MeDIL: A Python Package for Causal Modelling

Alex Markham, Aditya Chivukula, Moritz Grosse-Wentrup
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:621-624, 2020.

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 this Paper

BibTeX
@InProceedings{pmlr-v138-markham20a, 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}, pages = {621--624}, year = {2020}, editor = {Jaeger, Manfred and Nielsen, Thomas Dyhre}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/markham20a/markham20a.pdf}, url = {https://proceedings.mlr.press/v138/markham20a.html}, 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.} }
Endnote
%0 Conference Paper %T MeDIL: A Python Package for Causal Modelling %A Alex Markham %A Aditya Chivukula %A Moritz Grosse-Wentrup %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-markham20a %I PMLR %P 621--624 %U https://proceedings.mlr.press/v138/markham20a.html %V 138 %X 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.
APA
Markham, A., Chivukula, A. & Grosse-Wentrup, M.. (2020). MeDIL: A Python Package for Causal Modelling. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:621-624 Available from https://proceedings.mlr.press/v138/markham20a.html.

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