Comparar métodos

	Análise de Impacto Causal para Avaliação de Políticas	Diferenças em Diferenças (DiD)
Área	Inferência causal	Econometria
Família	Regression model	Regression model
Ano de origem	2015	1994
Autor original	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Bayesian counterfactual / time-series	Causal inference / panel regression
Fonte seminal	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomes	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	6	5
Resumo	Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy 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.