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

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

Causing: CAUsal INterpretation using Graphs

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

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

Code accompanying my 2021 ASA SDSS paper

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

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

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

Applications and validation analyses shown in the manuscript

Causality reading group

Project Risk Analysis

Investigation of network geometry and percolation in directed acyclic graphs (MSci Thesis). Maintained by Ariel Flint Ashery and Kevin Teo. Supervisor: Timothy Evans

CASCADE - CAncer Signaling CAusality DatabasE

Causal Abstraction of Neural Models Trained to Solve ReaSCAN