Spatio-Temporal Graphical Counterfactuals: An Overview
It tackles the problem of improving AI performance in new scenarios by providing a comprehensive overview and a novel framework for researchers in causal inference.
This paper presents a survey comparing existing counterfactual models and proposes a unified graphical causal framework to infer spatio-temporal counterfactuals, addressing the lack of graphical approaches for spatial and temporal interactions.
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.