cdt15/lingam

Python package for causal discovery based on LiNGAM.

What it solves

LiNGAM provides a way to estimate structural equation models or linear Bayesian networks. It specifically addresses the challenge of causal discovery—determining the causal relationships between variables—by leveraging the non-Gaussianity of the data to identify the direction of causality.

How it works

The library implements various algorithms (such as DirectLiNGAM, ICA-based LiNGAM, and RESIT) that analyze data to find a causal ordering of variables. By assuming the data is linear and non-Gaussian, the tool can distinguish between cause and effect in ways that standard correlation-based methods cannot. It can produce a causal order and an adjacency matrix representing the network of influences.

Who it’s for

This tool is designed for researchers and data scientists working with structural equation modeling, Bayesian networks, and causal inference, particularly those dealing with continuous, non-Gaussian data.

Highlights

  • Diverse Algorithm Support: Includes implementations for DirectLiNGAM, GroupDirectLiNGAM, and RESIT.
  • Time Series Capability: Supports causal discovery for time series via VAR-LiNGAM and VARMA-LiNGAM.
  • Handling Complex Data: Provides methods for longitudinal data, multiple datasets, and mixed data types.
  • Robustness: Includes algorithms like ParceLiNGAM and RCD to handle latent confounders and unobserved variables.

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