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math.DS2024
Minimum Reduced-Order Models via Causal Inference
Nan Chen, Honghu Liu
Constructing sparse, effective reduced-order models (ROMs) for high-dimensional dynamical data is an active area of research in applied sciences. In this work, we study an efficien…
stat.ME2024
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
Hongtao Zhu, Sizhe Zhang, Yang Su +2
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes in…