Bandingkan kaedah

	Bayesian Regression Discontinuity Design	Padanan Skor Kecenderungan
Bidang	Inferens Kausal	Statistik Penyelidikan
Keluarga	Regression model	Process / pipeline
Tahun asal	2004-2016	1983
Pengasas	Karabatsos & Walker; Chib & Jacobi	Paul Rosenbaum and Donald Rubin
Jenis	Bayesian causal inference / quasi-experimental	Method
Sumber perintis	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	Bayesian RDD, Bayesian RD, Bayes RDD, Bayesian regression-discontinuity	PSM, propensity score weighting, covariate balance
Berkaitan	5	3
Ringkasan	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.