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.

对于医学影像，因果生成建模因其能够回答干预和反事实查询而引起了人们的兴趣。本文主要研究了辅助分类器在生成逆事实图像时对干预效果的监督作用，发现了属性放大的问题，其中在干预过程中，不相关的属性会受到干扰，导致受保护特征和疾病状态之间的偏差。为了解决这个问题，我们提出了软对反事实微调的方法，大大减少了放大效应，并在大型胸部 X 射线数据集上验证了生成图像的有效性。本研究在医学影像的因果建模中迈出了重要一步，使得建模更加真实和无偏。

对抗性图像生成中的属性放大缓解

Mitigating attribute amplification in counterfactual image generation

We present a general causal generative modelling framework for accurate
estimation of high fidelity image counterfactuals with deep structural causal
models. Estimation of interventional and counterfactual queries for
high-dimensional structured variables, such as images, remains a challenging
task. We leverage ideas from causal mediation analysis and advances in
generative modelling to design new deep causal mechanisms for structured
variables in causal models. Our experiments demonstrate that our proposed
mechanisms are capable of accurate abduction and estimation of direct, indirect
and total effects as measured by axiomatic soundness of counterfactuals.

本研究提出了一种通用的因果生成建模框架，用于准确估计具有深度结构因果模型的高保真图像反事实情况。