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We introduce DoWhy-GCM, an extension of the DoWhy Python library, thatleverages graphical causal models. Unlike existing causality libraries, whichmainly focus on effect estimation questions, with DoWhy-GCM, users can ask awide range of additional causal questions, such as identifying the root causesof outliers and distributional changes, causal structure learning, attributingcausal influences, and diagnosis of causal structures. To this end, DoWhy-GCMusers first model cause-effect relations between variables in a system understudy through a graphical causal model, fit the causal mechanisms of variablesnext, and then ask the causal question. All these steps take only a few linesof code in DoWhy-GCM.  The library is available at https://github.com/py-why/dowhy.

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