DataFunSummit
Sep 3, 2023 · Artificial Intelligence
Estimating Clustered Data Causal Effects with DiConfounder: A Double‑Difference Framework
This article presents a comprehensive approach to estimating causal effects on clustered data using a double‑difference method, introduces the DiConfounder algorithm built on Rubin Causal Model extensions, details data characteristics, model assumptions, six‑step pipeline, and reports competitive results on the ACIC2022 challenge.
DiConfoundercausal inferenceclustered data
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