Sammenlign metoder

	Multiple Imputation	Propensity Score Matching
Fagområde	Statistik	Forskningsstatistik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1987	1983
Ophavsperson	Donald B. Rubin	Paul Rosenbaum and Donald Rubin
Type	Missing-data handling procedure	Method
Oprindelig kilde	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.	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.
Aliasser	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)	PSM, propensity score weighting, covariate balance
Relaterede	1	3
Resumé	Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.	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.