We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation, DoWhy-GCM addresses diverse causal queries, such as identifying the root causes of outliers and distributional changes, attributing causal influences to the data generating process of each node, or diagnosis of causal structures. With DoWhy-GCM, users typically specify cause-effect relations via a causal graph, fit causal mechanisms, and pose causal queries -- all with just a few lines of code. The general documentation is available at https://www.pywhy.org/dowhy and the DoWhy-GCM specific code at https://github.com/py-why/dowhy/tree/main/dowhy/gcm.

翻译：本文提出DoWhy-GCM——DoWhy Python库的扩展实现，该库基于图因果模型构建。与现有主要关注效应估计的因果推断库不同，DoWhy-GCM能够处理多样化的因果查询任务，例如识别异常值与分布变化的根本原因、追溯数据生成过程中各节点的因果影响，或进行因果结构诊断。使用DoWhy-GCM时，用户通常只需通过因果图指定因果关系、拟合因果机制并提出因果查询——所有操作仅需少量代码即可完成。通用文档详见https://www.pywhy.org/dowhy，DoWhy-GCM专项代码位于https://github.com/py-why/dowhy/tree/main/dowhy/gcm。