Welcome to MeDIL’s documentation!

MeDIL is a Python package for causal factor analysis, using the measurement dependence inducing latent (MeDIL) causal model framework [MGW20]. The package is under active development—see the develop branch of the repository on GitLab or its Github mirror.

Version:

1.1.0

Date:

Nov 20, 2024

Installation:

You can install the package from PyPI with the command pip install medil.

Features:

  • scikit-learn-style API

  • estimation of sparse causal factor structure and loadings in the linear Gaussian setting or more generally using a deep generative model [MLAS23]

  • \(\ell_0\)-penalized maximum likelihood estimation (BIC score-based search) for minimum MeDIL causal graphs in the linear Gaussian setting, as well as nonparametric constraint-based search using distance covariance or xi correlation

  • random generation of and sampling from linear Gaussian causal factor models

  • exact search for minimum edge clique cover (ECC) [GGfN09] as well as polynomial time heuristic using the one-pure-child assumption [MLAS23]

Further documentation:

Indices and tables