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교육 연구에서의 반사실적 영향 평가×성향 점수 매칭×
분야인과추론연구 통계
계열Regression modelProcess / pipeline
기원 연도2000s–2010s1983
창시자Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research CentrePaul Rosenbaum and Donald Rubin
유형Quasi-experimental causal inference frameworkMethod
원전Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115. DOI ↗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. DOI ↗
별칭CIE in education, counterfactual program evaluation, causal impact evaluation, education policy impact evaluationPSM, propensity score weighting, covariate balance
관련53
요약Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual: what would have happened to participants had they not been treated.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.
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ScholarGate방법 비교: Counterfactual Impact Evaluation in Education Research · Propensity Score Matching. 2026-06-20에 다음에서 검색함: https://scholargate.app/ko/compare