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

Abstract

In the information age, a paradigm revolution in applied data mining methods has emerged in response to the data explosion in management science research, but this artificial intelligence-based automated data-driven modeling process creates a new "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.