arXiv:2608. 02893v1 Announce Type: cross Abstract: Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables.
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arXiv:2607. 05620v1 Announce Type: cross Abstract: In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold.
arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.
The paper introduces counterfactual (CF) marginalisation, a test‑time evaluation method that assesses how robust classification models are to nuisance variables such as age or sex. By using a CF image generator to intervene on these parent variables, the method creates counterfactual versions of each test image and averages predictions over a chosen intervention distribution, yielding intervention‑aware predictions that filter out demographic effects while retaining patient‑specific latent information. These predictions are then used to define metrics for CF risk, calibration, stability, and worst‑case sensitivity, demonstrating the framework’s usefulness for quantitative robustness evaluation.
arXiv:2608. 08743v1 Announce Type: cross Abstract: Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time.
arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.
arXiv:2505. 08908v3 Announce Type: replace-cross Abstract: Many researchers apply classical statistical decision theory to evaluate treatment choices and learn optimal policies.