causal-networks

A Python library that helps data scientists to infer causation rather than observing correlation.

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

A resource list for causality in statistics, data science and physics

Spatial Empirical Dynamic Modeling

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Causing: CAUsal INterpretation using Graphs

Replication Resources (code+data) for "Causal Claims in Economics" by Garg P. and Fetzer T. (2026)

Implementation of Causation Entropy from Clarkson Center for Complex Systems Science (C3S2)

A Python package for drug discovery by analyzing causal paths on multiscale networks

Flash-P: Turning decades of biology into accurate causal networks with AI agents

A Brief Overview of Causal Inference (xaringan presentation)

Source code and data for "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery"

Experiments on Causality & Reinforcement Learning

Code and figures for the Differential Causal Inference (DCI) algorithm

A super light-weight web app to create causal loop diagrams (CLD) online. This is useful in Systems Thinking and System Dynamics.

Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.

Análise Robusta de Intervenções para Ansiedade com Técnicas de Tratamento de Dados Ausentes

Project Risk Analysis

Applications and validation analyses shown in the manuscript

A Python research framework and terminal-based IDE (RuleFlow Studio) for modeling, evolving, and analyzing discrete complex systems like ECA and SSS.