51 citations · 87 across the 14 of their papers we have counts for
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stat.ME2022★ 51 cited
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki +2
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 estimatio…
stat.ME2021
Why did the distribution change?
Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum +1
We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables. After factorizing the joint…