Introducing Causal Inference Using Bayesian Networks and "do"-Calculus

Autor/inn/en	Lu, Yonggang; Zheng, Qiujie; Quinn, Daniel
Titel	Introducing Causal Inference Using Bayesian Networks and "do"-Calculus
Quelle	In: Journal of Statistics and Data Science Education, 31 (2023) 1, S.3-17 (15 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lu, Yonggang)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1080/26939169.2022.2128118
Schlagwörter	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 + Suchen Sie Ihr Suchwort? Motivation for studies; Lernmotivation; Analysis; Differenzialrechnung; Infinitesimalrechnung; Integralrechnung; Fortgeschrittenenunterricht; Mathematics lessons; Mathematikunterricht; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Inferential statistics; Schließende Statistik; Teaching method; Lehrmethode; Unterrichtsmethode; Einführungskurs; Grafische Darstellung; Sexualität; Schwangerschaft; Colour; Farbbezeichnung; Farbe; Patient; Decision-making; Entscheidungsfindung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01