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stat.ME2022★ 1 cited
Degree of Interference: A General Framework For Causal Inference Under Interference
Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi
One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is unaffected by t…
stat.ME2022
Distributed Design for Causal Inferences on Big Observational Data
Yumin Zhang, Arman Sabbaghi
A fundamental issue in causal inference for Big Observational Data is confounding due to covariate imbalances between treatment groups. This can be addressed by designing the data…