Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
Advances in Neural Information Processing Systems , volume=
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Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
A generative framework using geometric diffusion for brain networks and tabular diffusion for other organs integrates ICD-coded SDoH proxies to improve disease reasoning on UK Biobank data.
citing papers explorer
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Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
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Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
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Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning
A generative framework using geometric diffusion for brain networks and tabular diffusion for other organs integrates ICD-coded SDoH proxies to improve disease reasoning on UK Biobank data.