论文标题
Dowhy-GCM：在图形因果模型中用于因果推断的Dowhy的扩展
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
论文作者
论文摘要
我们提出了Dowhy-GCM，这是Dowhy Python库的扩展，该图书馆利用图形因果模型。与现有的因果关系库（主要关注效应估计）不同，Dowhy-GCM解决了各种因果查询，例如确定异常值的根本原因和分布变化，将因果影响归因于每个节点的数据生成过程，或因果结构的诊断。使用Dowhy-GCM，用户通常通过因果图，拟合因果机制和姿势因果查询来指定因果关系 - 所有这些都只有几行代码。该通用文档可从https://www.pywhy.org/dowhy和https://github.com/py-why/dowhy/dowhy/tree/main/main/main/main/dowhy/gcm上获得。
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.
