Comparar métodos

	Análise Robusta de Impacto Causal	Análise Bayesiana de Impacto Causal
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2015	2015
Autor original	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
Tipo	Bayesian causal inference with robustness validation	Bayesian causal inference / time series
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.	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.
Outros nomes	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis
Relacionados	5	4
Resumo	Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.	Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect.