Salīdzināt metodes

	Sintētiskās kontroles metode izglītības pētniecībā	Propensity Score Matching
Nozare	Cēloņsakarību secināšana	Pētniecības statistika
Saime	Regression model	Process / pipeline
Izcelsmes gads	2003-2010	1983
Autors	Alberto Abadie, Alexis Diamond, and Jens Hainmueller	Paul Rosenbaum and Donald Rubin
Tips	Quasi-experimental causal inference	Method
Pirmavots	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.	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.
Citi nosaukumi	SCM in education, synthetic control, synthetic comparator, SCM	PSM, propensity score weighting, covariate balance
Saistītās	5	3
Kopsavilkums	The Synthetic Control Method (SCM) estimates the causal effect of an education policy or intervention by constructing a weighted combination of untreated comparison units — the synthetic control — that closely mimics the treated unit's pre-intervention trajectory. Developed by Abadie, Diamond, and Hainmueller, it is especially valuable when only one or a small number of schools, districts, or countries receive a policy change and no natural comparison exists.	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.