Porovnať metódy

	Politická evaluácia s instrumentálnymi premennými	Zodpovedajúce skóre sklonu
Odbor	Kauzálna inferencia	Štatistika vo výskume
Rodina	Regression model	Process / pipeline
Rok vzniku	1996 (modern policy-evaluation framing); IV roots 1920s	1983
Tvorca	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
Pôvodný zdroj	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.	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.
Ďalšie názvy	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	PSM, propensity score weighting, covariate balance
Príbuzné	5	3
Zhrnutie	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.