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To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy for mitigating such biases is to augment training data with synthetic images. Counterfactual (CF) generation is one such strategy, though the term is used in two different senses: in some works, CFs are produced through causality-based interventions derived from structural causal models, whereas in others, they are produced by non-causal image edits or conventional conditional generative models, such as altering anatomy or adding pathologies. In

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