So sánh phương pháp

	Mô hình phương trình cấu trúc Bayes (BSEM)	Thiết kế Gián đoạn Hồi quy (Regression Discontinuity Design - RDD)
Lĩnh vực	Bayes	Suy luận nhân quả
Họ	Bayesian methods	Regression model
Năm ra đời	2012	2008
Người khởi xướng	Bengt Muthén & Tihomir Asparouhov	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Loại	Bayesian latent variable model	Quasi-experimental causal design
Công trình gốc	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Tên gọi khác	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Liên quan	6	5
Tóm tắt	Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.