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

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

Applications and validation analyses shown in the manuscript

Project Risk Analysis

Causality reading group

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

CASCADE - CAncer Signaling CAusality DatabasE

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

Causal Abstraction of Neural Models Trained to Solve ReaSCAN

A Python package for learning and using causal networks via discrete geometry