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Found 2 papers, 1 papers with code
Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients
Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available.
Constraint-based Causal Structure Learning with Consistent Separating Sets
It is achieved by repeating the constraint-based causal structure learning scheme, iteratively, while searching for separating sets that are consistent with the graph obtained at the previous iteration.
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