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.

通过网络干预的主动学习方法，本论文旨在解决因果推断中潜在结果检验框架忽视个体干预以及假设独立处理效应的问题，提出了一种基于网络干预的主动学习方法以估计直接和溢出治疗效果，能够在复杂干预结构下具有高效和精确的效果估计能力。

在线实验中将主动学习与因果推断相结合的一种新方法

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

The aim of this paper is to make clear and precise the relationship between
the Rubin causal model (RCM) and structural causal model (SCM) frameworks for
causal inference. Adopting a neutral logical perspective, and drawing on
previous work, we show what is required for an RCM to be representable by an
SCM. A key result then shows that every RCM -- including those that violate
algebraic principles implied by the SCM framework -- emerges as an abstraction
of some representable RCM. Finally, we illustrate the power of this
ameliorative perspective by pinpointing an important role for SCM principles in
classic applications of RCMs; conversely, we offer a characterization of the
algebraic constraints implied by a graph, helping to substantiate further
comparisons between the two frameworks.

本论文旨在准确明确 Rubin 因果模型（RCM）和结构因果模型（SCM）框架之间的关系，揭示 SCMs 框架中隐含的代数约束规则，指出 SCM 原则在经典应用中的重要作用。