提出深度学习增强的因果推断方法，用于分析有社交关联人群的隔离政策随时间影响。
Estimating the treatment effect over time under general interference through deep learner integrated TMLE
- 结合时序模块与对抗训练，构建对干预不变的表示以减少偏倚
- 在模拟疫情模型中，估计误差更低，置信区间更窄，优于现有方法
- 适合研究具有网络关联的公共卫生政策效果，如隔离措施优化
理解具有潜在社交网络的人群中隔离政策的影响对公共健康至关重要，但大多数因果推断方法因假设个体独立而失效。本文提出 DeepNetTMLE，一种基于深度学习的靶向最大似然估计（TMLE）方法，用于在观测数据中估计受一般干扰影响的时间敏感处理效应。该方法通过引入时序模块和领域对抗训练，构建干预不变的表示，缓解随时间变化的混杂因素带来的偏倚。这一过程消除了当前处理与历史变量之间的关联，而靶向步骤保持了偏差-方差权衡，提升了反事实预测的可靠性。通过在不同隔离覆盖率下的‘易感-感染-康复’模型模拟实验，结果表明 DeepNetTMLE 在反事实估计中具有更低偏差和更精确的置信区间，可实现预算约束下的最优隔离策略推荐，显著优于现有先进方法。
原文摘要 · Abstract (English)
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to their assumption of independent individuals. We introduce DeepNetTMLE, a deep-learning-enhanced Targeted Maximum Likelihood Estimation (TMLE) method designed to estimate time-sensitive treatment effects in observational data. DeepNetTMLE mitigates bias from time-varying confounders under general interference by incorporating a temporal module and domain adversarial training to build intervention-invariant representations. This process removes associations between current treatments and historical variables, while the targeting step maintains the bias-variance trade-off, enhancing the reliability of counterfactual predictions. Using simulations of a ``Susceptible-Infected-Recovered'' model with varied quarantine coverages, we show that DeepNetTMLE achieves lower bias and more precise confidence intervals in counterfactual estimates, enabling optimal quarantine recommendations within budget constraints, surpassing state-of-the-art methods.
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