Comparar métodos

	Avaliação de Políticas com Variáveis Instrumentais	Propensity Score Matching
Área	Inferência causal	Estatística para pesquisa
Família	Regression model	Process / pipeline
Ano de origem	1996 (modern policy-evaluation framing); IV roots 1920s	1983
Autor original	Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)	Paul Rosenbaum and Donald Rubin
Tipo	Quasi-experimental causal inference / IV regression	Method
Fonte seminal	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.
Outros nomes	IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation	PSM, propensity score weighting, covariate balance
Relacionados	5	3
Resumo	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.