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.


  • constraint-based learning of minimum MeDIL causal graphs from marginal independence tests using distance covariance or xi correlation

  • estimation of causal factor loadings in the linear Gaussian setting or in the nonparametric setting using a variational autoencoder [MLAS23]

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

  • implementation of exact algorithm for minimum edge clique cover (ECC) [GGHuffnerN09] and wrapper for heuristic minimum ECC written in Java [CGM20]

Design principles:

  • scikit-learn style API

  • basic functionality with minimal dependencies (just NumPy) and optional dependencies for more functionality

  • as much as possible implemented using NumPy ``ndarray``s and methods for fast performance and wide compatibility





Oct 06, 2023

Indices and tables