Spatio-Temporal Graphical Counterfactuals: An Overview
<|reference_start|>Spatio-Temporal Graphical Counterfactuals: An Overview: Counterfactual thinking is a critical yet challenging topic for artificial intelligence to learn knowledge from data and ultimately improve their performances for new scenarios. Many research works, including Potential Outcome Model and Structural Causal Model, have been proposed to realize it. However, their modelings, theoretical foundations and application approaches are usually different. Moreover, there is a lack of graphical approach to infer spatio-temporal counterfactuals, that considers spatial and temporal interactions between multiple units. Thus, in this work, our aim is to investigate a survey to compare and discuss different counterfactual models, theories and approaches, and further build a unified graphical causal frameworks to infer the spatio-temporal counterfactuals.<|reference_end|>
@article{kang2024spatio-temporal,
    title={Spatio-Temporal Graphical Counterfactuals: An Overview},
    author={Mingyu Kang and Duxin Chen and Ziyuan Pu and Jianxi Gao and Wenwu Yu},
    journal={arXiv preprint arXiv:2407.01875},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01875},
    primaryClass={cs.AI}
}