A Python library that helps data scientists to infer causation rather than observing correlation.
-
Updated
May 21, 2024 - Python
A Python library that helps data scientists to infer causation rather than observing correlation.
Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and 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"
Code accompanying my 2021 ASA SDSS paper
Applications and validation analyses shown in the manuscript
CASCADE - CAncer Signaling CAusality DatabasE
An auto generator of alternative representations for Bayesian Networks.
Code and figures for the Differential Causal Inference (DCI) algorithm
Data processing procedure described in the article submitted to JBI Special Issue
Investigation of network geometry and percolation in directed acyclic graphs (MSci Thesis). Maintained by Ariel Flint Ashery and Kevin Teo. Supervisor: Timothy Evans
Experiments on Causality & Reinforcement Learning
Causal Abstraction of Neural Models Trained to Solve ReaSCAN
Causality reading group
Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
Add a description, image, and links to the causal-networks topic page so that developers can more easily learn about it.
To associate your repository with the causal-networks topic, visit your repo's landing page and select "manage topics."