  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.
