Salīdzināt metodes

	Politikas novērtējums Pretstatījuma ietekmes novērtējums (CIE)	Propensity Score Matching
Nozare	Cēloņsakarību secināšana	Pētniecības statistika
Saime	Regression model	Process / pipeline
Izcelsmes gads	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1983
Autors	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Paul Rosenbaum and Donald Rubin
Tips	Quasi-experimental causal evaluation	Method
Pirmavots	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.
Citi nosaukumi	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	PSM, propensity score weighting, covariate balance
Saistītās	5	3
Kopsavilkums	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.