เปรียบเทียบวิธี

	การวิเคราะห์ความไวต่ออคติแฝง (Rosenbaum Bounds / E-value)	การจับคู่คะแนนแนวโน้ม
สาขาวิชา	การอนุมานเชิงสาเหตุ	สถิติการวิจัย
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	2002	1983
ผู้ริเริ่ม	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)	Paul Rosenbaum and Donald Rubin
ประเภท	Sensitivity analysis for causal inference	Method
แหล่งต้นตำรับ	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	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	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity	PSM, propensity score weighting, covariate balance
ที่เกี่ยวข้อง	5	3
สรุป	Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017).	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.