Vertaile menetelmiä
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| Politiikka-arvioinnin vastafaktuaalinen vaikutusanalyysi (CIE)× | Propensity Score Matching× | |
|---|---|---|
| Tieteenala≠ | Kausaalipäättely | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe≠ | Regression model | Process / pipeline |
| Syntyvuosi≠ | 1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation) | 1983 |
| Kehittäjä≠ | Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines | Paul Rosenbaum and Donald Rubin |
| Tyyppi≠ | Quasi-experimental causal evaluation | Method |
| Alkuperäislähde≠ | 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. DOI ↗ |
| Rinnakkaisnimet≠ | CIE, policy CIE, counterfactual policy evaluation, impact evaluation | PSM, propensity score weighting, covariate balance |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | 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. |
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