Linganisha mbinu

	Uchambuzi Imara wa Athari za Kimaumbile	Uchambuzi wa Kitakwimu wa Athari za Kiusababishi kwa Kutumia Mbinu ya Bayesian
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015	2015
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
Aina	Bayesian causal inference with robustness validation	Bayesian causal inference / time series
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	4
Muhtasari	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.