Comparar métodos

	Impact Evaluation Design	Diferenças em Diferenças (DiD)
Área	Public Policy	Econometria
Família	Process / pipeline	Regression model
Ano de origem	2016	1994
Autor original	Development and program-evaluation community; codified by Gertler et al. (World Bank)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Design framework for causal impact evaluation	Causal inference / panel regression
Fonte seminal	Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. J. (2016). Impact Evaluation in Practice (2nd ed.). Washington, DC: World Bank. ISBN: 9781464807794	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomes	Impact Evaluation, Causal Impact Evaluation Design, Counterfactual Evaluation Design	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	3	5
Resumo	Impact evaluation design is the upstream task of structuring an evaluation so that it can credibly attribute changes in outcomes to a policy or program rather than to other factors. Its defining concern is the counterfactual: what would have happened to participants in the absence of the intervention. Codified in resources such as the World Bank's Impact Evaluation in Practice, the design process selects an identification strategy — randomised assignment, or a quasi-experimental method such as difference-in-differences, regression discontinuity, instrumental variables or matching — that constructs a valid comparison and yields an unbiased estimate of the intervention's effect.	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.