This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.
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May 10, 2024 - Python
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causal-graphical-models
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"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
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Sep 28, 2023 - Jupyter Notebook
Source Code for the Paper "Practical Algorithms for Orientations of Partially Directed Graphical Models"
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Mar 20, 2023 - Julia
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