CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
Causal inference in statistics: A primer
2 Pith papers cite this work. Polarity classification is still indexing.
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Graphical models show Equalized Odds and related fairness criteria to be misleading, so fairness assessments should be case-specific and depend on the algorithm's information flow.
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Causal Time Series Generation via Diffusion Models
CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
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Fairness criteria through the lens of directed acyclic graphical models
Graphical models show Equalized Odds and related fairness criteria to be misleading, so fairness assessments should be case-specific and depend on the algorithm's information flow.