Bayesian shrinkage priors on factor models produce sparse substitute confounders that support consistent regression-adjusted causal estimates under latent variable identification assumptions.
Estimating counterfactual treatment outcomes over time through adversarially balanced representations
3 Pith papers cite this work. Polarity classification is still indexing.
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Fed-CausalDiff proposes decoupled synchronization in a federated causal diffusion model to improve do-simulation and policy-value estimation across heterogeneous decentralized datasets.
A review proposing a unified framework for intervention-aware disease trajectory modeling in clinical AI, organized around three decision tasks and three data-generating mechanisms.
citing papers explorer
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Shrinkage priors for Bayesian Substitute Confounders
Bayesian shrinkage priors on factor models produce sparse substitute confounders that support consistent regression-adjusted causal estimates under latent variable identification assumptions.
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Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
Fed-CausalDiff proposes decoupled synchronization in a federated causal diffusion model to improve do-simulation and policy-value estimation across heterogeneous decentralized datasets.
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From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
A review proposing a unified framework for intervention-aware disease trajectory modeling in clinical AI, organized around three decision tasks and three data-generating mechanisms.