विधियों की तुलना करें

	नीति मूल्यांकन प्रति-तथ्यात्मक प्रभाव मूल्यांकन (CIE)	प्रोपेंसिटी स्कोर मैचिंग
क्षेत्र	कारणात्मक अनुमान	अनुसंधान सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1983
प्रवर्तक	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Paul Rosenbaum and Donald Rubin
प्रकार	Quasi-experimental causal evaluation	Method
मौलिक स्रोत	Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881	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.
उपनाम	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	PSM, propensity score weighting, covariate balance
संबंधित	5	3
सारांश	Counterfactual Impact Evaluation (CIE) for policy assessment estimates the causal effect of a public policy or programme by comparing observed outcomes of participants against a rigorously constructed counterfactual — what would have happened had the policy not existed. Rooted in the Rubin potential-outcomes framework, CIE is the standard methodology endorsed by the European Commission for evaluating research, innovation, and structural funding programmes.	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.