uhlerlab/graphical_model_learning
Learning graphical models, with a focus on causal models and learning from interventional data.
This project helps data scientists, statisticians, and researchers understand complex relationships between variables from observed data, especially when some data comes from experiments or interventions. It takes in observational or experimental datasets and outputs a visual representation (a graph) that shows which variables influence each other. This is for professionals who need to map out cause-and-effect relationships in their field.
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Use this if you need to discover the underlying causal structure between variables in your data, particularly when you have a mix of observational data and data from controlled experiments.
Not ideal if you are looking for a simple predictive model without needing to understand the causal mechanisms or if your data doesn't involve potential cause-and-effect relationships.
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HTML
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Last pushed
Jun 14, 2022
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