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| Ocena polityki z wykorzystaniem zmiennych instrumentalnych× | Dopasowanie wyników skłonności× | |
|---|---|---|
| Dziedzina≠ | Wnioskowanie przyczynowe | Statystyka w badaniach |
| Rodzina≠ | Regression model | Process / pipeline |
| Rok powstania≠ | 1996 (modern policy-evaluation framing); IV roots 1920s | 1983 |
| Twórca≠ | Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953) | Paul Rosenbaum and Donald Rubin |
| Typ≠ | Quasi-experimental causal inference / IV regression | Method |
| Źródło pierwotne≠ | Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455. 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 ↗ |
| Inne nazwy≠ | IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation | PSM, propensity score weighting, covariate balance |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | Instrumental Variables (IV) estimation for policy evaluation is a quasi-experimental technique that uses an exogenous instrument — a variable that shifts exposure to a policy but is otherwise unrelated to the outcome — to recover the causal effect of a program or intervention from non-experimental data. Popularised in policy research by Angrist, Imbens, and Rubin (1996), it identifies the Local Average Treatment Effect (LATE) among units whose treatment status is changed by the instrument. | 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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