DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
This provides a more versatile tool for researchers and practitioners in causal inference, though it is incremental as it builds on the existing DoWhy library.
The authors tackled the limitation of existing causality libraries focusing mainly on effect estimation by introducing DoWhy-GCM, an extension of the DoWhy Python library that enables diverse causal queries like root cause identification and causal structure diagnosis, resulting in a tool that allows users to specify causal graphs, fit mechanisms, and pose queries with just a few lines of code.
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.