Integrating Active Learning in Causal Inference with Interference: A
  Novel Approach in Online Experiments
Hongtao Zhu Sizhe Zhang Yang Su Zhenyu Zhao Nan Chen
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, where treatments on neighbors within connected
networks affect one's outcomes, and (2) non-random treatment assignments
influenced by confounders. To improve the efficiency of estimating potentially
complex effects functions, we introduce an novel active learning approach:
Active Learning in Causal Inference with Interference (ACI). This approach uses
Gaussian process to flexibly model the direct and spillover treatment effects
as a function of a continuous measure of neighbors' treatment assignment. The
ACI framework sequentially identifies the experimental settings that demand
further data. It further optimizes the treatment assignments under the network
interference structure using genetic algorithms to achieve efficient learning
outcome. By applying our method to simulation data and a Tencent game dataset,
we demonstrate its feasibility in achieving accurate effects estimations with
reduced data requirements. This ACI approach marks a significant advancement in
the realm of data efficiency for causal inference, offering a robust and
efficient alternative to traditional methodologies, particularly in scenarios
characterized by complex interference patterns.