An index of algorithms for learning causality with data

Eliot: the logging system that tells you *why* it happened

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

Python package for causal discovery based on LiNGAM.

YLearn, a pun of "learn why", is a python package for causal inference

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

Hyper-geometric computational causality for Rust

A Python package for causal inference using Synthetic Controls

💊 Comparing causality methods in a fair and just way.

Python package for Granger causality test with nonlinear forecasting methods.

Causing: CAUsal INterpretation using Graphs

A project for exploring differentially active signaling paths related to proteomics datasets

가짜연구소 <인과추론과 실무> 프로젝트

Tigramite is a time series analysis python module for linear and information-theoretic causal inference. Version 3.0 described in http://arxiv.org/abs/1702.07007 is available at https://github.com/jakobrunge/tigramite!

Causal Relation Extraction and Identification using Conditional Random Fields

Mendelian Randomization with Biomarker Associations for Causality with Outcomes

金融文本中的原因事件

Causal Inference Using Quasi-Experimental Methods

Identifying reasons for human actions in lifestyle vlogs.

RL-based Causal Discovery with Prior Knowledge

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

Researching causal relationships in time series data using Temporal Convolutional Networks (TCNs) combined with attention mechanisms. This approach aims to identify complex temporal interactions. Additionally, we're incorporating uncertainty quantification to enhance the reliability of our causal predictions.

Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.

This project contains the implementation of the Dimensional Causality method proposed in Bayesian inference of causal relations between dynamical systems (Benkő, Zsigmond ; Zlatniczki, Ádám* ; Stippinger, Marcell* ; Fabó, Dániel ; Sólyom, András ; Erőss, Loránd ; Telcs, András** ; Somogyvári, Zoltán; https://doi.org/10.1016/j.chaos.2024.115142)

Code base for our paper "Connectivity-Inspired Network for Context-Aware Recognition" (ECCV 2024, Human-inspired Computer Vision workshop)

MATLAB module for assessing the causal links between time series

VAC^2: Visual Analysis of Combined Causality in Event Sequences

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

This repository contains the code for the paper "Sentiment-driven statistical causality in multimodal systems", by Ioannis Chalkiadakis, Anna Zaremba, Gareth W. Peters and Michael J. Chantler.

Code accompanying my 2021 ASA SDSS paper