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A meticulously curated collection of cutting-edge research, frameworks, and methodologies in Causal Artificial Intelligence.

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🌟 Awesome Causal AI

The Ultimate Compendium for Causal Intelligence & Reasoning

Awesome License: CC BY 4.0 GitHub stars PRs Welcome Last Updated Resources

🎯 "From Correlation to Causation: Decoding the Why Behind AI"

The most comprehensive collection of Causal AI resources
Meticulously curated by researchers, for researchers

📚 Explore Resources • 🤝 Contribute • 📧 Contact • ⭐ Star to Bookmark

🚀 Quick Start

graph TD
    A[🔍 Interested in Causal AI?] --> B{What's your goal?}
    B -->|Research| C[📚 Browse Papers & Surveys]
    B -->|Implementation| D[🛠️ Explore GitHub Repos]
    B -->|Learning| E[📖 Check Educational Resources]
    C --> F[🎯 Find your domain]
    D --> F
    E --> F
    F --> G[🌟 Start Contributing!]
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🏆 Featured Highlights

📊 Repository Statistics
  • 🔗 GitHub Repositories: 1,000+ (sorted by popularity)
  • 📄 Research Papers: 100+ (latest from ArXiv)
  • ⭐ Combined Stars: 50,000+ across all repositories
  • 🔄 Last Updated: September 5, 2025

🥇 Top Libraries

🎓 Learning Path

  1. Beginner: Start with survey papers
  2. Intermediate: Explore core libraries
  3. Advanced: Dive into latest research
  4. Expert: Contribute to open source
  5. Master: Publish your own work

💡 Why Causal AI Matters

🤔 The Great Divide

� Traditional ML ⚡ Causal AI
"What will happen?" "What if we DO something?"
Predicts the future Shapes the future
Finds patterns Uncovers mechanisms
Correlation hunter Causation detective 🕵️‍♀️

🌟 Real-World Magic

🏥 Healthcare
"Should I give this drug to THIS patient?"
→ Personalized medicine that saves lives
💼 Business
"Will this marketing campaign actually work?"
→ Stop wasting money on ineffective strategies
🌍 Policy & Economics
"What happens if we change this law?"
→ Evidence-based governance that actually helps
🤖 AI & Robotics
"Why did the robot fail?"
→ Intelligent agents that understand consequences
🔬 Scientific Discovery
"What's the real mechanism here?"
→ Breakthrough insights into how the world works

🚀 GitHub Repositories

  • causalml (5568 ⭐) - Uplift modeling and causal inference with machine learning algorithms (Python)
  • EconML (4272 ⭐) - EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. (Jupyter Notebook)
  • awesome-causality-algorithms (3183 ⭐) - An index of algorithms for learning causality with data (Unknown)
  • causallib (786 ⭐) - A Python package for modular causal inference analysis and model evaluations (Python)
  • causalML (776 ⭐) - The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML (Jupyter Notebook)
  • causal-ml (737 ⭐) - Must-read papers and resources related to causal inference and machine (deep) learning (Unknown)
  • Causal_Reading_Group (517 ⭐) - We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week. (Unknown)
  • arXausality (416 ⭐) - A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past. (Python)
  • CEVAE (345 ⭐) - Causal Effect Inference with Deep Latent-Variable Models (Python)
  • Deep-Learning-for-Causal-Inference (336 ⭐) - Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2 and Pytorch. (Unknown)
  • irl-maxent (294 ⭐) - Maximum Entropy and Maximum Causal Entropy Inverse Reinforcement Learning Implementation in Python (Jupyter Notebook)
  • deepscm (287 ⭐) - Repository for Deep Structural Causal Models for Tractable Counterfactual Inference (Jupyter Notebook)
  • causalML-teaching (260 ⭐) - This repository consolidates my teaching material for "Causal Machine Learning". (HTML)
  • Computational-Social-Science-Training-Program (251 ⭐) - This repo contains all of the materials for Sociology 273, Computational Social Science Parts A/B. Designed as part of Berkeley's Computational Social Science Training Program. (Jupyter Notebook)
  • STream3R (233 ⭐) - Dynamic 3D Foundation Model using Causal Transformer (Python)
  • Machine-Learning (208 ⭐) - Machine Learning and Causal Inference taught by Brigham Frandsen (Jupyter Notebook)
  • robustdg (175 ⭐) - Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks. (Python)
  • CausalTransformer (154 ⭐) - Code for the paper "Causal Transformer for Estimating Counterfactual Outcomes" (Python)
  • Awesome-Uplift-Model (145 ⭐) - How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？(Jupyter Notebook)
  • causality (143 ⭐) - Notes, exercises and other materials related to causal inference, causal discovery and causal ML. (Jupyter Notebook)
  • cxplain (131 ⭐) - Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model. (Python)
  • csle (130 ⭐) - A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and causal inference. (Python)
  • MetricsMLNotebooks (128 ⭐) - Notebooks for Applied Causal Inference Powered by ML and AI (Jupyter Notebook)
  • awesome-marketing-machine-learning (118 ⭐) - A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more (Unknown)
  • Awesome-Causal-Inference (108 ⭐) - A curated list of awesome work on causal inference, particularly in machine learning. (Unknown)
  • CGNN (100 ⭐) - Replication code for the article "Learning Functional Causal Models with Generative Neural Networks" (Python)
  • Awesome-Causal-RL (99 ⭐) - A curated list of causal reinforcement learning resources. (Unknown)
  • Causality-in-Trustworthy-Machine-Learning (95 ⭐) - The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions. (Python)
  • RL-Causality (89 ⭐) - References at the Intersection of Causality and Reinforcement Learning (Unknown)
  • iap-cidl (88 ⭐) - Causal Inference & Deep Learning, MIT IAP 2018 (Unknown)
  • CausalMatch (87 ⭐) - CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in causal inference. (Jupyter Notebook)
  • ENCO (85 ⭐) - Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints" (Python)
  • Awesome-Causality-Inspired-GNNs (82 ⭐) - An awesome collection of causality-inspired graph neural networks. (Unknown)
  • drone_causality (80 ⭐) - Training, data processing, and analysis code used for the paper "Robust Visual Flight Navigation with Liquid Neural Networks". (Python)
  • OpenASCE (77 ⭐) - OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package. (Python)
  • causal-semantic-generative-model (74 ⭐) - Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21) (Python)
  • shapFlex (74 ⭐) - An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model (R)
  • causal-transformer-decoder (73 ⭐) - This repository contains the code for the causal transformer decoder, which is the autoregressive version of the Pytorch TransformerDecoder. (Python)
  • ICML2021-Gem (68 ⭐) - Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks." (Jupyter Notebook)
  • PACT (65 ⭐) - Perception-Action Causal Transformer (Python)
  • cdml-neurips2020 (62 ⭐) - This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural I. (Python)
  • causeinfer (61 ⭐) - Machine learning based causal inference/uplift in Python (Python)
  • BiLLM (61 ⭐) - Tool for converting LLMs from uni-directional to bi-directional by removing causal mask for tasks like classification and sentence embeddings. (Python)
  • cai-causal-graph (60 ⭐) - A Causal AI package for causal graphs. (Python)
  • CMCRL (54 ⭐) - The official implementation of “Cross-Modal Causal Representation Learning for Radiology Report Generation” (Python)
  • CITRIS (53 ⭐) - Code repository of the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" and "iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects" (Python)
  • CausalFormer (52 ⭐) - PyTorch Implementation of CausalFormer: An Interpretable Transformer for Temporal Causal Discovery (Jupyter Notebook)
  • ACE (51 ⭐) - Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective. (Jupyter Notebook)
  • CausalAI-Course (45 ⭐) - Lectures and Tutorials for the Causal AI course (Jupyter Notebook)
  • Awesome-Causal-Reinforcement-Learning (44 ⭐) - Official repository of "A Survey on Causal Reinforcement Learning" (Unknown)
  • RUN (35 ⭐) - Official repository of "Root Cause Analysis In Microservice Using Neural Granger Causal Discovery" @ AAAI 2024 (Python)
  • awesome-deep-causal-learning (18 ⭐) - A curated list of awesome deep causal learning methods since 2018 (Unknown)
  • awesome-Causal-RL-papers (18 ⭐) - Here is a list of papers related to causal reinforcement learning, and I hope you can submit relevant missing papers in the issue. (Unknown)
  • TCS (12 ⭐) - Causal Inference using Deep Bayesian Dynamic Survival Models (Jupyter Notebook)
  • ML4C (12 ⭐) - ML4C: Seeing Causality Through Latent Vicinity (Python)
  • awesome-causal-learning (11 ⭐) - Causality with machine learning, topic including causal represenation learning, causal reinforcement learning (Unknown)
  • Awesome-Causal-Discovery (11 ⭐) - An awesome list of Causality and Machine Learning related papers, books and other resources. (Unknown)
  • causalML (6 ⭐) - Causal random forest example (Python)
  • Causal-AI-Driven-Model-for-Predicting-Telecom-Churn-and-Boosting-Retention (1 ⭐) - This project proposes an improved churn prediction model for telecoms using Causal AI to identify root causes rather than mere correlations. It evaluates XGBoost, ANN, and DNN models, revealing key churn factors like contract type and payment method. Results show high accuracy and actionable insights to boost retention and business sustainability. (Python)
📚 Educational Resources & Personal Projects (Click to expand)

Note: This section contains educational resources, tutorials, course materials, personal projects, and research implementations. While these may not be production-ready libraries, they can be valuable for learning causal AI concepts and exploring different approaches.

🎓 Educational & Tutorial Resources

  • causal_ai (50 ⭐) - This project introduces Causal AI and how it can drive business value. (Jupyter Notebook)
  • causality-tutorials (48 ⭐) - Short tutorials on the use of machine learning methods for causal inference (Jupyter Notebook)
  • max-causal-ent-irl (48 ⭐) - Maximum Causal Entropy Inverse Reinforcement Learning (Python)
  • CausalMBRL (48 ⭐) - Official data and code for our paper Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning (Python)
  • DisC (41 ⭐) - NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure (Python)
  • CausalCuriosity (40 ⭐) - Official implementation of Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning at ICML 2021. (Python)
  • AAAI2022-HCM (39 ⭐) - Source code of AAAI'22 paper: A Hybrid Causal Structure Learning Algorithm for Mixed-type Data (Python)
  • nips-ad-placement-challenge (38 ⭐) - The winning solution to the Ad Placement Challenge (NIPS'17 Causal Inference and Machine Learning Workshop) (TeX)
  • causalDML (36 ⭐) - Implementation of Double Machine Learning (R)
  • BISCUIT (36 ⭐) - Official code of the paper "BISCUIT: Causal Representation Learning from Binary Interactions" (UAI 2023) (Python)
  • leap (36 ⭐) - LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network. (Jupyter Notebook)
  • De-focus-Attention-Networks (35 ⭐) - Learning 1D Causal Visual Representation with De-focus Attention Networks (Python)
  • GRADER (34 ⭐) - This is the official implementation of NeurIPS 2022 paper "Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning" (Python)
  • plsc-40601-CI-ML (34 ⭐) - Advanced Topics in Causal Inference course. (TeX)
  • sherlock (32 ⭐) - R package for causal machine learning for segment discovery and analysis (R)
  • Double-Debiased-Adversary (32 ⭐) - Official PyTorch Implementation for "Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning" (Python)
  • causal-mbrl (32 ⭐) - Toolkit of Causal Model-based Reinforcement Learning. (Python)
  • cf-feasibility (31 ⭐) - Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers" (Python)
  • weakly-supervised-causal-representation-learning (31 ⭐) - This repository contains the code for the paper "Weakly supervised causal representation learning" by Johann Brehmer, Pim de Haan, Phillip Lippe, and Taco Cohen, published at NeurIPS 2022. (Python)
  • Deep-Learning-and-Causal-Inference (30 ⭐) - MIT course repo. (Unknown)
  • Explainable-Causal-Reinforcement-Learning (30 ⭐) - Explainable Causal Reinforcement Learning with attention (Python)
  • causaltriplet (30 ⭐) - [CLeaR23] Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning (Python)
  • spectral-rl2 (28 ⭐) - Representation Learning (RepL) Methods in Reinforcement Learning and Causal Inference (Python)
  • PDGrapher (28 ⭐) - Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks (Jupyter Notebook)
  • Toybox (27 ⭐) - The Machine Learning Toybox for testing the behavior of autonomous agents. (Python)
  • causalglm (26 ⭐) - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning (R)
  • 2024-causal-inference-machine-learning (25 ⭐) - This repository contains lab materials from the course "Introduction to Causal Inference in Machine Learning" from Spring 2024 at New York University. (Jupyter Notebook)
  • orthoml (25 ⭐) - Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Semenova, Goldman, Chernozhukov, Taddy (2017) https://arxiv.org/abs/1712.09988 (R)
  • CausalRepID (25 ⭐) - Experiments to reproduce results in Interventional Causal Representation Learning. (Python)
  • CATEs (24 ⭐) - Machine Learning Estimation of Heterogeneous Causal Effects (R)
  • deep-ei (23 ⭐) - Tools for examining the causal structure of artificial neural networks with information theory (Jupyter Notebook)
  • SMLW-Causality-Tutorial (22 ⭐) - Eastern European Machine Learning Summer School (EEML) Workshop Series 2022. Tutorial on Causality for the Serbian Machine Learning Workshop on Deep Learning and Reinforcement Learning. (Jupyter Notebook)
  • ssl-causal (22 ⭐) - Semi-Supervised Learning for Deep Causal Generative Models (Python)
  • aicp (21 ⭐) - Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", by Juan L Gamella and Christina Heinze-Deml. (Python)
  • CauseBox (21 ⭐) - Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensignificant advances through the application of machine learningtechniques, especially deep neural networks. Unfortunately, to-datemany of the proposed methods are evaluated on different (data,software/hardware, hyperparameter) setups and consequently it isnearly impossible to compare the efficacy of the available methodsor reproduce results presented in original research manuscripts. In this paper, we propose a causal inference toolbox (CauseBox)that addresses the aforementioned problems. At the time of thewriting, the toolbox includes seven state of the art causal inferencemethods and two benchmark datasets. By providing convenientcommand-line and GUI-based interfaces, theCauseBoxtoolboxhelps researchers fairly compare the state of the art methods intheir chosen application context against benchmark datasets. (Python)
  • mlcausality (21 ⭐) - Nonlinear Granger causality using machine learning techniques (Python)
  • bg_control (20 ⭐) - Improving short-term prandial blood glucose outcomes for people with type 1 diabetes, a complex disease that affects nearly 10 million people worldwide. We aim to leverage semi-supervised learning to identify unlabelled meals in time-series blood glucose data, develop meal-scoring functions, and explore causal machine-learning techniques. (Jupyter Notebook)
  • Targeted-BEHRT (20 ⭐) - Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records (Python)
  • COR (19 ⭐) - Causal Representation Learning for Out-of-Distribution Recommendation. (Python)
  • CausalMIL (19 ⭐) - Multi-Instance Causal Representation Learning (Python)
  • Causal-Inference-on-Networked-Data (19 ⭐) - Code for paper "Estimating Causal Effects on Networked Observational Data via Representation Learning" (Python)
  • causaldiscovery-latent-interventions (19 ⭐) - Method based on neural networks and variational inference for causal discovery under latent interventions, i. e. learning a shared causal graph among a infinite mixture (under a Dirichlet process prior) of intervention structural causal models . (Python)
  • pypsps (18 ⭐) - Predictive State Propensity Subclassification (PSPS): A causal deep learning algoritm in TensorFlow keras (Jupyter Notebook)
  • MolReactGen (17 ⭐) - Auto-regressive causal language model for molecule (SMILES) and reaction template (SMARTS) generation based on the Hugging Face implementation of OpenAI's GPT-2 transformer decoder model (Jupyter Notebook)
  • SPACE (16 ⭐) - This repository contains the source codes for the paper: "SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments" published at ICCV 2021, 1st SEAI Workshop. (Python)
  • 14.388_py (16 ⭐) - This material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey’s Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages. (Jupyter Notebook)
  • CausalBandits (16 ⭐) - Project on Causal Machine learning CS 7290 (Python)
  • ccbo (16 ⭐) - This repo contains the code associated to the paper: "Constrained Causal Bayesian Optimization" by Aglietti Virginia, Alan Malek, Ira Ktena, and Silvia Chiappa. International Conference on Machine Learning. PMLR, 2023. (Python)
  • Fine-Grained-Causal-RL (16 ⭐) - Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning (ICML 2024) (Python)

👤 Personal Projects & Implementations

  • Causal-U-Net (38 ⭐) - unofficial PyTorch implementation of 《A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement》 (Python)
  • CauAIN (22 ⭐) - Code for IJCAI 2022 accepted paper titled "CauAIN: Causal Aware Interaction Network for Emotion Recognition in Conversations" (Python)
  • cfrnet-reproduction (18 ⭐) - Reproducing Shalit et al.'s Individual Treatment Effect model. This is a deep neural net that can be applied to various problems in causal inference. (Python)
  • gnn-causality-research (16 ⭐) - Exploring Causal Inferences in Finance with Graph Neural Networks (Unknown)
  • EXPLAIGNN (14 ⭐) - Code for our SIGIR 2023 paper. EXPLAIGNN provides a pipeline for conversational question answering (ConvQA) over heterogeneous sources, and code for iterative graph neural networks (GNNs). Such iterative GNNs can help to causally explaignn GNN outputs. (Python)
  • causal_nets (13 ⭐) - Implementation of neural network algorithm for estimation of heterogeneous treatment effects and propensity scores described in Farrell, Liang, and Misra (2021) (Python)
  • VCIN (12 ⭐) - Authors's code for "Variational Causal Inference Network for Explanatory Visual Question Answering" and "Integrating Neural-Symbolic Reasoning with Variational Causal Inference Network for Explanatory Visual Question Answering" (Python)
  • UNet-MISO (10 ⭐) - unofficial implementation of "A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement" (Python)
  • swap-graphs (8 ⭐) - An implementation of input swap graphs. A tool to discover the role of neural network components with causal interventions. (Python)
  • nanoDPO (6 ⭐) - A nimble and innovative implementation of the Direct Preference Optimization (DPO) algorithm with Causal Transformer and LSTM model, inspired by the paper of DPO in fine-tuning unsupervised Language Models (Python)
  • Aitia (5 ⭐) - Implementation of the paper "Aitia: Efficient Secure Computation for Causal Discovery (Python)
  • Causal_CNN (3 ⭐) - This repository includes supplementary material to the manuscript Ghasempour, Moosavi and de Luna (2023, Convolutional neural networks for valid and efficient causal inference). (Unknown)
  • CCD_MLIC (2 ⭐) - Unofficial PyTorch implementation of the paper "Contextual Debiasing for Visual Recognition with Causal Mechanisms" (Python)
  • Bias_AIPW (1 ⭐) - Accompanying material to Model misspecification and bias for inverse probability weighting estimators of average causal effects (R)
  • paper_ai_agent_safety (1 ⭐) - This repository provides an implementation of our paper Causal Analysis of Agent Behavior for AI Safety. (Python)
  • CausalML (1 ⭐) - This is a collection of papers related to causality. (HTML)
  • paper_CEE-efficient-ML (1 ⭐) - Code for paper "Efficient and Globally Robust Causal Excursion Effect Estimation" by Zhaoxi Cheng, Lauren Bell, Tianchen Qian (R)
  • causal-conjugation (1 ⭐) - Code for the paper "Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number" in EMNLP Findings 2023. (Python)
  • causality (0 ⭐) - A Streamlit app documenting attempts to simulate phenomenal causality with AI methods. (HTML)
  • causality (0 ⭐) - causality framework for building isomorphic neural net based machine learning service (JavaScript)

🔬 Research & Experimental Projects

  • aipwML (5 ⭐) - Regression adjustment, IPW, and AIPW estimators for causal effects using various ML methods (HTML)
  • spph504-007 (5 ⭐) - SPPH 504 (section 007): Application of Epidemiological Methods (HTML)
  • long-context-transformers (4 ⭐) - A repository to get train transformers to access longer context for causal language models, most of these methods are still in testing. Try them out if you'd like but please lmk your results so we don't duplicate work :) (Python)
  • firm-network (3 ⭐) - Source code, data and plots for our paper "Analysis of Large Market Data Using Neural Networks: A Causal Approach" (Python)
  • AGI-Alignment-and-Safety-Research (1 ⭐) - Investigating AGI alignment, safety, and ethical considerations in AI systems through causal reasoning, red-teaming prompts, and fairness evaluations across large language models (LLMs). (Python)
  • transformer-fault-diagnosis (1 ⭐) - Causal NOTEARS method with counterfactual inference (Python)

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👤 Author & Maintainer

Amir Rafe

Texas State University, AI in Transportation Lab

Email University Research

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A meticulously curated collection of cutting-edge research, frameworks, and methodologies in Causal Artificial Intelligence.

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