CDVD: Causal Dynamic Variational Deconfounder for Estimating Parameter Adjusting Effect

Load balancing technology, involving the adjustment of Configuration and Optimization Parameters (COPs), is crucial for enhancing network performance. Improper parameter adjustment directly impacts network Key Performance Indicators (KPIs). Therefore, it is essential to estimate the effect of parameter adjustment. Recent methods indicate that incorporating causal inference into parameter adjusting effect estimation contributes to building more robust models. However, the causation studies remain difficult. Due to the incomplete observation of the data, unobserved covariates lead to spurious correlations between COPs and KPIs, making the results of the model bias in practical scenarios. In this paper, we define the task of COP-KPI modeling under causal inference and design the Causal Dynamic Variational Deconfounder (CDVD) for estimating the effect of parameter adjustment based on a Variational Autoencoder-Bidirectional Long Short-Term Memory (VAE-BiLSTM) hybrid model. Specifically, leveraging a variational autoencoder, we learn the representation of hidden confounders using observed covariates. Subsequently, we apply this representation to predict KPIs values. Domain adversarial approach is used to mitigate confounding bias, achieving unbiased modeling of COP-KPI. We conduct experiments on synthetic datasets and real-world datasets. Experiments show that the proposed CDVD can improve predictive performance by 65 % on real-world wireless network datasets. We also demonstrate the robustness of our CDVD against paramater adjustment.