juangamella/causal-chamber-paper
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
This project helps AI researchers evaluate new methodologies against real-world, causal data. It takes raw sensor data, images, and other physical system measurements from 'causal chambers' and provides code to reproduce various case studies, enabling the comparison of different AI algorithms. Researchers developing new AI methods would use this to validate their approaches.
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Use this if you are an AI researcher looking for a standardized, physical system-based testbed to evaluate and compare new causal discovery, OOD generalization, changepoint detection, or symbolic regression algorithms.
Not ideal if you are looking for an off-the-shelf AI solution to apply to your own data, as this project focuses on providing a testbed for developing and evaluating new AI methodologies.
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MIT
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Last pushed
Jan 03, 2025
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