neildhir/DCBO
Dynamic causal Bayesian optimisation (NeurIPS 2021)
This tool helps scientists and operations researchers make a sequence of optimal decisions in complex systems that change over time. It takes in observational data and past actions from a system (like a biological process or a supply chain) and suggests the best next action to take to optimize a specific outcome. This is useful for anyone managing dynamic systems where understanding cause and effect is crucial for effective intervention.
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Use this if you need to make repeated, optimal interventions in a system where the relationships between factors and the system's state are constantly evolving.
Not ideal if your system is static, if you don't need to understand causal relationships, or if you are looking for a fully robust, production-ready software solution.
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40
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Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 24, 2023
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