Modeling Interference for Treatment Effect Estimation in Network Dynamic Environment

In recent years, estimating causal effects of treatment on the outcome variable in network environments has attracted growing interest. The intrinsic interconnectedness of network and the attendant violation of the SUTVA assumption have prompted a wave of treatment effect estimation methods tailored to network settings, yielding considerable progress such as capturing hidden confounders by leveraging auxiliary network structure. Nevertheless, despite these advances, the existing methods: (i) mainly focus on the static network, overlooking the dynamic nature of many real-world networks and confounders that evolve over time; (ii) assume the absence of dynamic network interference where one unit’s treatment can affect its neighbors’ outcomes. To address these two limitations, we first define a new estimand of treatment effects accounting for interference in a dynamic network environment, i.e., CATE-ID, and establish its identifiability under such an environment. Then we accordingly propose DSPNET, a framework tailored specifically for treatment effect estimation in dynamic network environment, that leverages historical information and network structure to capture time-varying confounders and model dynamic interference. Extensive experiments demonstrate the superiority of our proposed method compared to state-of-the-art approaches.