In networks, effective dynamic treatment allocation requires deciding both whom to treat and also when, so as to amplify policy impact through spillovers. An early intervention at a well-connected node can trigger cascades that change which nodes are worth targeting in the next period. Existing treatment strategies under network interference are largely static while dynamic treatment frameworks typically ignore network structure altogether. We integrate these perspectives and propose Q-Ising, a three-stage pipeline that (i) estimates network adoption dynamics via a Bayesian dynamic Ising model from a single observed panel, (ii) augments treatment adoption histories with continuous posterior latent states, and (iii) learns a dynamic policy via offline reinforcement learning. The Bayesian mechanism enables uncertainty quantification over dynamic decisions, yielding posterior ensemble policies with interpretable spillover estimates. We provide a finite-sample regret upper bound that decomposes into standard offline-RL uncertainty, network abstraction error, and first stage error in Ising state estimation. We apply our method to data from Indian village microfinance networks and synthetic stochastic block models under simulated heterogeneous susceptible-infected-susceptible (SIS) dynamics and demonstrate that adaptive targeting outperforms static centrality benchmarks.

翻译：在网络中，有效的动态处理分配需要同时决定处理对象与时机，以通过溢出效应放大政策影响。对高度连接节点的早期干预可能引发级联效应，进而改变下一时期值得瞄准的节点。现有网络干扰下的处理策略大多为静态，而动态处理框架通常完全忽略网络结构。我们整合这两类视角，提出Q-Ising三阶段流程：(i) 通过贝叶斯动态Ising模型从单个观测面板估计网络采用动态；(ii) 用连续后验潜状态增强处理采用历史；(iii) 通过离线强化学习学习动态策略。贝叶斯机制实现了对动态决策的不确定性量化，生成了具有可解释溢出估计的后验集成策略。我们给出了有限样本遗憾上界，该界限分解为标准离线强化学习不确定性、网络抽象误差以及Ising状态估计第一阶段误差。我们将该方法应用于印度村庄小额金融网络数据以及异质易感-感染-易感（SIS）动态模拟下的合成随机块模型，并证明自适应靶向优于静态中心性基准。