পদ্ধতির তুলনা করুন

	শিক্ষা গবেষণায় প্রতিঘটনা-ভিত্তিক প্রভাব মূল্যায়ন	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	কার্যকারণ অনুমান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2000s–2010s	1983
প্রবর্তক	Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre	Paul Rosenbaum and Donald Rubin
ধরন	Quasi-experimental causal inference framework	Method
মৌলিক উৎস	Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115.	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.
অপর নাম	CIE in education, counterfactual program evaluation, causal impact evaluation, education policy impact evaluation	PSM, propensity score weighting, covariate balance
সম্পর্কিত	5	3
সারসংক্ষেপ	Counterfactual 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.