เปรียบเทียบวิธี

	Inverse Probability Weighting (IPW / IPTW) การถ่วงน้ำหนักด้วยความน่าจะเป็นผกผัน	การถดถอยโลจิสติก
สาขาวิชา	การอนุมานเชิงสาเหตุ	สถิติการวิจัย
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	2000	1958
ผู้ริเริ่ม	Robins, Hernán & Brumback	David Roxbee Cox
ประเภท	Causal inference weighting estimator	Method
แหล่งต้นตำรับ	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
ชื่อเรียกอื่น	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting	logit model, binomial logistic regression, LR
ที่เกี่ยวข้อง	5	3
สรุป	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.