Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
在存在干扰的因果推断中引入主动学习：在线实验的新方法
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…