Spatio-Temporal Graphical Counterfactuals: An Overview
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911460104339456 |
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| author | Kang, Mingyu Chen, Duxin Pu, Ziyuan Gao, Jianxi Yu, Wenwu |
| author_facet | Kang, Mingyu Chen, Duxin Pu, Ziyuan Gao, Jianxi Yu, Wenwu |
| contents | Counterfactual thinking is a crucial yet challenging topic for artificial intelligence to learn knowledge from data and ultimately improve performance for new scenarios. Many research works, including the Potential Outcome Model (POM) and the Structural Causal Model (SCM), have been proposed to address this. However, their modeling, theoretical foundations, and application approaches often differ. Moreover, there is a lack of graphical approaches for inferring spatio-temporal counterfactuals, that account for spatial and temporal interactions among multiple units. Thus, in this work, we aim to present a survey that compares and discusses different counterfactual models, theories and approaches. Additionally, we propose a unified graphical causal framework to infer spatio-temporal counterfactuals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_01875 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal Graphical Counterfactuals: An Overview Kang, Mingyu Chen, Duxin Pu, Ziyuan Gao, Jianxi Yu, Wenwu Artificial Intelligence Counterfactual thinking is a crucial yet challenging topic for artificial intelligence to learn knowledge from data and ultimately improve performance for new scenarios. Many research works, including the Potential Outcome Model (POM) and the Structural Causal Model (SCM), have been proposed to address this. However, their modeling, theoretical foundations, and application approaches often differ. Moreover, there is a lack of graphical approaches for inferring spatio-temporal counterfactuals, that account for spatial and temporal interactions among multiple units. Thus, in this work, we aim to present a survey that compares and discusses different counterfactual models, theories and approaches. Additionally, we propose a unified graphical causal framework to infer spatio-temporal counterfactuals. |
| title | Spatio-Temporal Graphical Counterfactuals: An Overview |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.01875 |