phlippe/BISCUIT
Official code of the paper "BISCUIT: Causal Representation Learning from Binary Interactions" (UAI 2023)
This project helps researchers and scientists understand the underlying cause-and-effect relationships in complex systems. It takes raw observational data from interactive environments, like robotic simulations or embodied AI platforms, and processes it to reveal distinct causal variables. The primary users are researchers in fields like robotics, AI, and reinforcement learning who need to dissect causal factors from observed binary interactions.
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Use this if you are a researcher working with binary interaction data from environments like robotic arms or simulated agents and need to discover causal representations.
Not ideal if your data does not involve binary interactions or if you are not working within research domains that focus on causal representation learning.
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Python
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Last pushed
Mar 12, 2024
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