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.

翻译：在因果推断研究领域，主流潜在结果框架——特别是鲁宾因果模型（RCM）——常忽视个体间的干扰，并假设处理效应相互独立。然而，这一假设往往与现实世界中复杂的情境相悖，其中干扰不仅是可能的，而且是普遍存在的。本研究致力于解决这一偏差，聚焦于在以下两种假设下估计直接效应与溢出效应：（1）基于网络的干扰，即相连网络中邻居的处理会影响个体的结果；（2）受混杂因素影响的非随机处理分配。为提升潜在复杂效应函数的估计效率，我们提出了一种新的主动学习方法：含干扰因果推断中的主动学习（ACI）。该方法利用高斯过程灵活建模直接效应与溢出效应，将其表示为邻居处理分配连续度量的函数。ACI框架依次识别需要进一步数据的实验设置，并利用遗传算法优化网络干扰结构下的处理分配，以实现高效的学习结果。通过将我们的方法应用于模拟数据及腾讯游戏数据集，我们证明其能在减少数据需求的同时实现准确的效应估计。这一ACI方法标志着因果推断数据效率领域的重大进展，为传统方法提供了稳健且高效的替代方案，尤其适用于具有复杂干扰模式的场景。