So sánh phương pháp

	Thiết kế Hồi quy Gián đoạn Bayes	Thiết kế gián đoạn hồi quy mờ
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2004-2016	2001
Người khởi xướng	Karabatsos & Walker; Chib & Jacobi	Hahn, Todd & van der Klaauw
Loại	Bayesian causal inference / quasi-experimental	Quasi-experimental causal inference
Công trình gốc	Karabatsos, G., & Walker, S. G. (2004). Coherent inference in regression discontinuity designs with a Bayesian nonparametric approach. Journal of the American Statistical Association, 99(468), 1121-1131.	Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.
Tên gọi khác	Bayesian RDD, Bayesian RD, Bayes RDD, Bayesian regression-discontinuity	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Liên quan	5	5
Tóm tắt	Bayesian Regression Discontinuity Design (Bayesian RDD) embeds the classical RD framework — which estimates a local causal effect at a known assignment cutoff — within a Bayesian inferential engine. Prior distributions are placed on the regression functions on either side of the cutoff and on the treatment-effect parameter, yielding a full posterior distribution over the causal estimand rather than a single point estimate with a frequentist p-value.	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.