156 citations · 235 across the 11 of their papers we have counts for
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stat.ME2026
A Unified Approach to Interpretable Causal Root Cause Attribution
Jing Zhou, Dominik Janzing, Sepp Tsang +2
Understanding why a target metric changes is a fundamental problem in data-driven decision making, beyond anomaly detection alone. We study root cause attribution for metric change…
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…