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	Robust Causal Impact Analysis	Analiza wpływu przyczynowego
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2015	2015
Twórca	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Typ	Bayesian causal inference with robustness validation	Bayesian causal inference / counterfactual forecasting
Źródło pierwotne	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.
Inne nazwy	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Pokrewne	5	5
Podsumowanie	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.	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.