Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
Abstract
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.
Cite
@article{arxiv.2407.19688,
title = {Causal Interventional Prediction System for Robust and Explainable Effect Forecasting},
author = {Zhixuan Chu and Hui Ding and Guang Zeng and Shiyu Wang and Yiming Li},
journal= {arXiv preprint arXiv:2407.19688},
year = {2024}
}
Comments
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24), October 21--25, 2024, Boise, ID, USA