A Data-driven Causality Modeling Framework: An Empirical Study of Modeling the Effect of Indoor Air Quality Perception on Students' Cognitive Performance

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@Article{YANG:2023:procs,

• author = "Guangfei Yang and Bing Yan",
• title = "A Data-driven Causality Modeling Framework: An Empirical Study of Modeling the Effect of Indoor Air Quality Perception on Students' Cognitive Performance",
• journal = "Procedia Computer Science",
• volume = "221",
• pages = "839--844",
• year = "2023",
• note = "Tenth International Conference on Information Technology and Quantitative Management (ITQM 2023)",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2023.08.059",
• URL = "https://www.sciencedirect.com/science/article/pii/S1877050923008177",
• keywords = "genetic algorithms, genetic programming, Casual Graph, Data Driven, Structural Equation Model, Artificial Intelligence, Relationship Modeling",
• abstract = "big relationship' problem, where a large number of correlations obfuscate the identification of true causal relationships. In this paper, we use a causality modeling framework that combines correlation modeling and causality pruning processes. First, symbolic regression is used to model white-box correlations of human intelligence, and then spurious correlations that do not conform to causal graph theory are pruned so that ultimately causal relationships and explicit candidate models describing these relationships can be found automatically. In an empirical research problem, the framework is compared with a traditional hypothesis construction-validation process, and the results are consistent between the two. The proposed framework implements a data-driven 'correlation+causation' automatic modeling capability, which will greatly improve modeling efficiency and reliability",

}

Genetic Programming entries for Guangfei Yang Bing Yan

Citations