Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
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AI 根据所提供的论文材料归类：摘要明确研究网络干扰下的直接与溢出处理效应，并优化网络中的处理分配。
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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
- 研究问题
- 在网络邻居处理产生溢出、处理分配又受到混杂影响时，如何更节省数据地估计直接效应和溢出效应？
- 方法与思路
- 作者提出 ACI，以高斯过程刻画直接及溢出效应与邻居处理连续指标的关系，序贯选择补充数据并用遗传算法优化分配。
- 材料陈述的贡献
- 摘要称模拟与腾讯游戏数据展示了方法的可行性，能够以较少数据获得准确效应估计；具体改善幅度未在摘要中给出。
- 阅读关注点
- 应核对网络暴露指标、非随机分配下的识别条件，以及主动采样和遗传算法优化怎样影响估计目标与评价。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未说明混杂调整与序贯设计的完整条件，不能确认因果有效性或普遍优势。
原文摘要
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.