Estimating Causal Effect Trajectories under Dynamic Network Interference

Dynamic network interference combines unknown propagation strengths and delays with treatment–outcome feedback. We propose DyNICL (Dynamic Network Interference Causal Learning) to estimate direct, peer, total, and interaction effect trajectories. Four fixed stochastic policies define the effects. Learned hop–lag exposure and history representations support neural backward-Q regression, with Q-based and assignment-weighted augmented estimation. Under sequential exchangeability, positivity, and exposure sufficiency, we identify the policy values and establish Q-based consistency under continuation convergence. For augmented estimation, we derive a cross-fitted orthogonal expansion separating continuation–assignment product error from conditional exposure drift. DyNICL-Q achieves lower peer and total effect RMSE than adapted NetEst and TNet across all nine synthetic neighbor-count/lag settings. On two semi-synthetic contact-network settings, DyNICL-Aug achieves the lowest peer and total effect RMSE, while DyNICL-Q achieves the lowest direct effect RMSE.