Linganisha mbinu

	Uchambuzi wa Athari za Kimajukumu za Kisababishi zenye Tofauti	Njia ya Kidhibiti Sanisi (SCM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015-2016	2003–2010
Mwanzilishi	Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Aina	Causal inference / heterogeneous effects estimation	Quasi-experimental causal inference
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.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Majina mbadala	HTE-CausalImpact, CATE causal impact, heterogeneous causal impact, subgroup causal impact analysis	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Zinazohusiana	5	4
Muhtasari	Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment effect (CATE) estimation, it reveals which groups benefit most or least from an intervention.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.