DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

TL;DR

DoWhy-GCM extends the DoWhy library to handle diverse causal questions using graphical models, enabling users to specify causal graphs, fit mechanisms, and perform analyses with minimal code.

Contribution

It introduces DoWhy-GCM, a novel extension that broadens causal inference capabilities beyond effect estimation to include root cause analysis and causal structure diagnosis.

Findings

Supports diverse causal queries with graphical models

Enables causal analysis with minimal code

Provides comprehensive documentation and open-source code

Abstract

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.