  Although the widespread use of AI systems in today's world is growing, many
current AI systems are found vulnerable due to hidden bias and missing
information, especially in the most commonly used forecasting system. In this
work, we explore the robustness and explainability of AI-based forecasting
systems. We provide an in-depth analysis of the underlying causality involved
in the effect prediction task and further establish a causal graph based on
treatment, adjustment variable, confounder, and outcome. Correspondingly, we
design a causal interventional prediction system (CIPS) based on a variational
autoencoder and fully conditional specification of multiple imputations.
Extensive results demonstrate the superiority of our system over
state-of-the-art methods and show remarkable versatility and extensibility in
practice.
