M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Review of Causal Discovery Methods Based on Graphical Models
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A method that translates causal relationships into a Bipolar Argumentation Framework and applies semi-stable semantics to generate explanatory feature sets for machine learning predictions.
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.
citing papers explorer
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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A Causal Argumentation Method for Explainability of Machine Learning Models
A method that translates causal relationships into a Bipolar Argumentation Framework and applies semi-stable semantics to generate explanatory feature sets for machine learning predictions.
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Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.