Causal reasoning provides a language to ask important interventional and
counterfactual questions beyond purely statistical association. In medical
imaging, for example, we may want to study the causal effect of genetic,
environmental, or lifestyle factors on the normal and pathological variation of
anatomical phenotypes. However, while anatomical shape models of 3D surface
meshes, extracted from automated image segmentation, can be reliably
constructed, there is a lack of computational tooling to enable causal
reasoning about morphological variations. To tackle this problem, we propose
deep structural causal shape models (CSMs), which utilise high-quality mesh
generation techniques, from geometric deep learning, within the expressive
framework of deep structural causal models. CSMs enable subject-specific
prognoses through counterfactual mesh generation ("How would this patient's
brain structure change if they were ten years older?"), which is in contrast to
most current works on purely population-level statistical shape modelling. We
demonstrate the capabilities of CSMs at all levels of Pearl's causal hierarchy
through a number of qualitative and quantitative experiments leveraging a large
dataset of 3D brain structures.

提出了利用深度结构性因果形态模型（CSMs）的计算工具来实现对形态变化的因果推理，通过反事实网格生成产生个体化的预测，从而研究基因、环境和生活方式因素对人体结构的影响。