Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis (MS). Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like when demographic or disease covariates are changed. These images can be used for modeling disease progression or used for downstream image processing tasks where controlling for confounders is necessary.

中文翻译：

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多发性硬化症MR图像的结构因果模型

精密医学涉及回答反事实问题，例如“该患者对治疗A或治疗B的反应会更好吗？” 这些类型的问题本质上是因果关系，需要使用因果推断工具来回答，例如，使用结构因果模型（SCM）。在这项工作中，我们开发了一个SCM，该模型为多发性硬化症（MS）的人的人口统计学信息，疾病协变量和磁共振（MR）图像之间的相互作用建模。SCM中的推论会生成反事实图像，该图像显示出人口统计或疾病协变量发生变化时大脑的MR图像的外观。这些图像可用于建模疾病进展，或用于需要控制混杂因素的下游图像处理任务。