DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. (arXiv:2206.06821v1 [stat.ME])

We introduce DoWhy-GCM, an extension of the DoWhy Python library, that
leverages graphical causal models. Unlike existing causality libraries, which
mainly focus on effect estimation questions, with DoWhy-GCM, users can ask a
wide range of additional causal questions, such as identifying the root causes
of outliers and distributional changes, causal structure learning, attributing
causal influences, and diagnosis of causal structures. To this end, DoWhy-GCM
users first model cause-effect relations between variables in a system under
study through a graphical causal …

arxiv causal inference dowhy extension inference