Linganisha mbinu

	Propensity Score Matching in Education	Uundaji wa Thamani Iliyoongezwa
Nyanja	Education	Saikometriki
Familia	Process / pipeline	Latent structure
Mwaka wa asili	1983	1998
Mwanzilishi	Rosenbaum & Rubin (method); educational application widespread (Stuart and others)	William Sanders, Sandra Horn
Aina	Observational causal inference by matching treated and untreated units on treatment probability	Longitudinal student achievement modeling
Chanzo asilia	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.	Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615-631.
Majina mbadala	Educational Propensity Score Matching, PSM in Education, Propensity Matching for School Effects, Observational Causal Matching	VAM
Zinazohusiana	4	4
Muhtasari	Propensity score matching estimates the causal effect of an educational treatment from observational data by pairing treated students, schools, or teachers with comparison units that had the same probability of receiving the treatment given their observed characteristics. Introduced by Rosenbaum and Rubin, it collapses many confounding variables into a single score and matches on it, approximating the balance a randomized experiment would create. In education — where randomizing program participation, retention, or school choice is often impossible — it is a widely used quasi-experimental tool.	Value-Added Modeling (VAM) is a method for assessing the contribution of schools or teachers to student achievement growth, developed by Sanders and Horn (1998). VAM isolates the effect of a teacher or school by comparing student gains (value added) while controlling for prior achievement and student characteristics.