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Estimativa Duplamente Robusta Multi-Período×Diferenças em Diferenças (DiD)×
ÁreaInferência causalEconometria
FamíliaRegression modelRegression model
Ano de origem1994-20211994
Autor originalRobins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TipoSemiparametric causal estimatorCausal inference / panel regression
Fonte seminalBang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomeslongitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimationdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados65
ResumoMulti-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point.Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.
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ScholarGateComparar métodos: Multi-period Doubly Robust Estimation · Difference-in-Differences. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare