网络干扰下的保形个体治疗效果估计
Conformal Individual Treatment Effect Estimation under Networked Interference
- EURECOM（欧洲通信学院）
- Northeastern University London（伦敦东北大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究提出干扰调整的加权保形预测方法，放宽无干扰假设，通过构造可观测上界，为网络干扰下的反事实结果和个体治疗效果提供有限样本覆盖保证。
中文摘要 AI 辅助
保形反事实预测在无干扰假设下，为反事实结果和个体治疗效果构建具有有限样本覆盖保证的预测集。在本工作中，我们放宽了这一假设，允许每个单元的可能结果依赖于其他单元的治疗和协变量。在此设定下，倾向得分重加权无法恢复加权可交换性，现有方法可能无法实现有效覆盖。为解决此问题，我们开发了干扰调整的加权保形预测，通过构造目标干预下理想但不可观测的保形 $p$ 值的可观测上界来考虑干扰。所得预测集在转导和归纳设定下，为反事实结果和个体治疗效果提供有限样本边际覆盖保证。当干预引起的非一致性分数变化有界时，我们还推导出更精确的构造。数值实验表明，我们的方法保持名义覆盖，而现有方法可能无法做到。
英文摘要
Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-interference assumption. In this work, we relax this assumption by allowing each unit's potential outcomes to depend on other units' treatments and covariates. In this setting, propensity-score reweighting does not restore weighted exchangeability, and existing methods may fail to achieve valid coverage. To address this issue, we develop interference-adjusted weighted conformal prediction that accounts for interference by constructing an observable upper bound on the ideal and unobserved conformal $p$-value under the target intervention. The resulting prediction sets provide finite-sample marginal coverage guarantees for counterfactual outcomes and individual treatment effects in both transductive and inductive settings. We also derive a sharper construction when intervention-induced changes in nonconformity scores are bounded. Numerical experiments show that our methods preserve nominal coverage, whereas existing methods may not.
