เปรียบเทียบวิธี

	การถ่วงน้ำหนักด้วยคะแนนแนวโน้ม (Propensity Score Weighting - PSW / IPW)	การจับคู่คะแนนแนวโน้ม
สาขาวิชา	การอนุมานเชิงสาเหตุ	สถิติการวิจัย
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	1983 (propensity score); 2003 (efficient IPW estimator)	1983
ผู้ริเริ่ม	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Paul Rosenbaum and Donald Rubin
ประเภท	Causal inference / reweighting	Method
แหล่งต้นตำรับ	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.	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.
ชื่อเรียกอื่น	PSW, inverse probability weighting, IPW, propensity-based weighting	PSM, propensity score weighting, covariate balance
ที่เกี่ยวข้อง	6	3
สรุป	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).	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.