Popular GitHub Repositories for Causal Discovery

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Causal discovery algorithms and tools for implementing new ones

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Amortized Inference for Causal Structure Learning, NeurIPS 2022

Active Bayesian Causal Inference (Neurips'22)

Code for "LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis" paper

[TMLR23] FedDAG: Federated DAG Structure Learning

A python package for finding causal functional connectivity from neural time series observations.

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.

[ICLR 2023] ReScore: Boosting Causal Discovery via Adaptive Sample Reweighting

This repository focuses on advancing the process of causal graph generation by integrating the capabilities of Large Language Models (LLMs) and time-tested algorithms from causal theory.

Neural Time-Invariant Causal Discovery from Time Series Data

Ordered Causal Discovery (two-staged causal structure discovery with Deep Learning)

Python package for CITS algorithm: Causal inference from time series data

Repository for our paper: "Improving Reinforcement Learning-based Autonomous Agents with Causal Models", published at PRIMA 2024

FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning

Experiments for the paper "Learning Causality Structures from Electricity Demand Data" using causal discovery tools to improve energy demand forecasting using energy consumption data from Argentina.

基于因果分析的DoWhy反事实网络模型：用于分析免费增值游戏流失的工程合成数据