Comparar métodos

	Análisis de Impacto Causal con Datos de Panel	Diferencias en Diferencias con Datos de Panel (Panel DiD / TWFE)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2015 (base method); panel extension mid-2010s	1985–2004
Autor original	Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature	Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004)
Tipo	Bayesian structural time-series causal inference	Causal inference / panel regression
Fuente 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
Alias	Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis	Two-Way Fixed Effects DiD, TWFE, Panel DiD, Panel Diff-in-Diff
Relacionados	6	4
Resumen	Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods.	Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units.