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AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Counterfactual explanations formalize "what-if" scenarios by identifying modifications to an input instance that obtain a desired alternative prediction. Traditionally, whether generated via instance-specific optimization or amortized single pass models, these approaches rely on a single, deterministic point-estimate predictor. However, this ignores predictive uncertainty and hypothesis variability, leading to brittle explanations that frequently become invalid if the underlying model is retrained or updated. To address this fragility, we propose the Amortized Variational Counterfactual Genera

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