phlippe/CITRIS
Code repository of the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" and "iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects"
This project helps researchers and scientists understand the underlying cause-and-effect relationships in dynamic visual systems, like how different factors influence an object's movement or changes in a game. You input sequences of images where you've made specific changes (interventions), and it outputs the hidden causal factors at play, even when effects are rapid. This is for researchers in AI, robotics, or causal inference who work with visual data.
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Use this if you are a researcher studying complex systems from video or image sequences and need to identify the independent causal variables and their relationships, especially when interventions are possible.
Not ideal if you don't have temporal sequences of data, cannot perform interventions, or are not working with visual observations.
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58
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10
Language
Python
License
BSD-3-Clause-Clear
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Last pushed
Jun 16, 2023
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