Bandingkan metode

	Pembobotan Skor Kecenderungan Bayesian	Pembobotan Skor Propensitas (PSW / IPW)
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	2009	1983 (propensity score); 2003 (efficient IPW estimator)
Pencetus	McCandless, Gustafson & Austin	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tipe	Bayesian causal weighting estimator	Causal inference / reweighting
Sumber perintis	McCandless, L. C., Gustafson, P., & Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28(1), 94–112.	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 PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
Terkait	6	6
Ringkasan	Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yields fully probabilistic uncertainty intervals for the average treatment effect, accounting for the uncertainty in both the score model and the outcome.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).