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What is Clustering Challenges in DiD Analysis about?

This lecture discusses the challenges of conducting causal inference using Difference-in-Differences (DiD) with a small number of clusters, highlighting issues related to clustering and common shocks. It reviews various model-based approaches and alternative methodologies for inference, emphasizing the importance of assumptions regarding treatment effects and error terms. The lecture also addresses the appropriate level for clustering in DiD analyses and references key literature on the topic.

Author

kanspurchase2

Language

EN