This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.
- Updated
May 10, 2024 - Python
#
causal-graphical-models
Here are 5 public repositories matching this topic...
"Evaluating Digital Agriculture Recommendations with Causal Inference". It was accepted and presented in the special track on Artificial Intelligence for Social Impact, AAAI-23
machine-learning sustainability recommendation-system causal-inference earth-observation decision-support-system observational-data causal-graphical-models digital-agriculture
- Updated
Sep 28, 2023 - Jupyter Notebook
Implémentation d’un système d’IA Explicable (XAI) basé sur les explications contrastives bi-factuelles, avec optimisations algorithmiques et interface graphique CausaLytics.
causal-models explainable-ai xai casual-inference counterfactual-explanations contrastive-explanations causal-graphs causal-graphical-models causal-graph counterfactual-contrastive-learning
- Updated
Aug 30, 2025 - Jupyter Notebook
Source Code for the Paper "Practical Algorithms for Orientations of Partially Directed Graphical Models"
- Updated
Mar 20, 2023 - Julia
🧠 Implement a bi-factual contrastive explanation system for AI decisions, enhancing understanding through formal definitions and optimized algorithms.
causal-models explainable-ai xai casual-inference counterfactual-explanations contrastive-explanations causal-graphs causal-graphical-models causal-graph counterfactual-contrastive-learning
- Updated
Nov 19, 2025 - Jupyter Notebook
Improve this page
Add a description, image, and links to the causal-graphical-models topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the causal-graphical-models topic, visit your repo's landing page and select "manage topics."