Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

arXiv:2402.12710v1 Announce Type: cross
Abstract: In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, …

abstract active learning arxiv causal inference cs.lg domain effects framework independent inference interference novel research stat.me stat.ml treatment type world