ERIC Number: EJ1384375
Record Type: Journal
Publication Date: 2023
Pages: 15
Abstractor: As Provided
ISBN: N/A
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EISSN: EISSN-2693-9169
Available Date: N/A
Introducing Causal Inference Using Bayesian Networks and "do"-Calculus
Lu, Yonggang; Zheng, Qiujie; Quinn, Daniel
Journal of Statistics and Data Science Education, v31 n1 p3-17 2023
We present an instructional approach to teaching causal inference using Bayesian networks and "do"-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal inference with an emphasis on probabilistic reasoning and causal assumption. It also reveals the relevance and distinction between causal and statistical inference. Using a freeware tool, we demonstrate our approach with five examples that instructors can use to introduce students at different levels to the conception of causality, motivate them to learn more concepts for causal inference, and demonstrate practical applications of causal inference. We also provide detailed suggestions on using the five examples in the classroom.
Descriptors: Bayesian Statistics, Learning Motivation, Calculus, Advanced Courses, Mathematics Instruction, Probability, Statistical Inference, Attribution Theory, Teaching Methods, Introductory Courses, Graphs, Logical Thinking, Statistics Education, Sexuality, Pregnancy, Color, Preferences, Cultural Traits, Patients, Drug Therapy, Outcomes of Treatment, Decision Making
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Publication Type: Journal Articles; Reports - Descriptive
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Language: English
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