Salīdzināt metodes

	Propensity Score Matching in Education	Modelēšana, balstoties uz pievienoto vērtību
Nozare	Education	Psihometrija
Saime	Process / pipeline	Latent structure
Izcelsmes gads	1983	1998
Autors	Rosenbaum & Rubin (method); educational application widespread (Stuart and others)	William Sanders, Sandra Horn
Tips	Observational causal inference by matching treated and untreated units on treatment probability	Longitudinal student achievement modeling
Pirmavots	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.
Citi nosaukumi	Educational Propensity Score Matching, PSM in Education, Propensity Matching for School Effects, Observational Causal Matching	VAM
Saistītās	4	4
Kopsavilkums	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.