MeDIL: A Python Package for Causal Modelling

MeDIL: A Python Package for Causal Modelling

Abstract

We present the 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 MeDIL software package are especially suited for analyzing data from fields such as psychometric, epidemiology, etc. that rely on questionnaire or survey data.

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Authors
  • Markham, Alex
  • Chivukula, Aditya
  • Grosse-Wentrup, Moritz
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
PGM 2020 - The 10th International Conference on Probabilistic Graphical Models
Divisions
Neuroinformatics
Event Location
Aalborg
Event Type
Conference
Event Dates
23.-25.09.2020
Date
September 2020
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