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stat.ME2026
Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
Matthew Eichhorn, Samir Khan, Johan Ugander +1
Variance reduction for causal inference in the presence of network interference is often achieved through either outcome modeling, typically analyzed under unit-randomized Bernoull…
stat.ME2025
Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference
Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu
Estimating causal effects under interference is pertinent to many real-world settings. Recent work with low-order potential outcomes models uses a rollout design to obtain unbiased…