Causal generative modelling is gaining interest in medical imaging due to its ability to answer interventional and counterfactual queries. Most work focuses on generating counterfactual images that look plausible, using auxiliary classifiers to enforce effectiveness of simulated interventions. We investigate pitfalls in this approach, discovering the issue of attribute amplification, where unrelated attributes are spuriously affected during interventions, leading to biases across protected characteristics and disease status. We show that attribute amplification is caused by the use of hard labels in the counterfactual training process and propose soft counterfactual fine-tuning to mitigate this issue. Our method substantially reduces the amplification effect while maintaining effectiveness of generated images, demonstrated on a large chest X-ray dataset. Our work makes an important advancement towards more faithful and unbiased causal modelling in medical imaging.

翻译：因果生成建模因能回答干预性与反事实查询，正引起医学影像领域的关注。多数工作聚焦于生成视觉合理的反事实图像，并借助辅助分类器确保模拟干预的有效性。我们探究了该方法的潜在缺陷，发现属性放大问题——即干预过程中无关属性被虚假影响，导致受保护特征与疾病状态间的偏差。研究表明，属性放大源于反事实训练过程中硬标签的使用，并提出软反事实微调方法以缓解该问题。在大规模胸部X光数据集上的实验证明，本方法在显著降低放大效应的同时，保持了生成图像的有效性。该工作为医学影像中更可靠、无偏的因果建模推进了重要一步。