Bayesian Network for Discovering the Potential Causal Structure in Observational Data

Cody S. Ding ()
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Cody S. Ding: University of Missouri-St. Louis

Chapter Chapter 11 in Dependent Data in Social Sciences Research, 2024, pp 259-286 from Springer

Abstract: Abstract Bayesian network analysis, a sophisticated branch of machine learning, is a powerful method for unraveling intricate causal relationships in observational datasets. Based on principles from Bayesian statistics, this approach goes beyond mere pattern recognition, delving into the realm of causation by modeling the probabilistic conditional dependencies among variables. This chapter discusses the logic of using Bayesian network analysis as a causal discovery tool in social science research, explaining underlying assumptions necessary for identifying possible causal structures. In essence, Bayesian networks serve as graphical representations of conditional dependencies among variables, encapsulating a visual manifestation of the cause-and-effect dynamics inherent in the data. This distinctive capability makes Bayesian network analysis invaluable in discerning causal structures, shedding light on the factors that drive observed patterns and phenomena, facilitating a clear understanding of the intricate web of relationships, enabling researchers and practitioners to derive meaningful insights, and making informed decisions based on a nuanced understanding of the causal mechanisms at play. We also discussed assessing the Bayesian network model fit using the structure equation modeling approach and how to make inferences and predictions based on the identified structure. Bayesian network analysis finds application across diverse fields, such as healthcare, finance, and environmental science. This chapter used a real-world example to illustrate the utility of the Bayesian network in exploring how adverse social events impact our mental health, identifying factors influencing the onset of mood disorder symptoms, and offering a foundation for targeted interventions.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-56318-8_11

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DOI: 10.1007/978-3-031-56318-8_11

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