Compară metode

	Analiza de impact cauzal al efectului eterogen al tratamentului	Analiza seriilor de timp întrerupte (ITS)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2015-2016	2002
Autorul original	Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tip	Causal inference / heterogeneous effects estimation	Quasi-experimental segmented regression
Sursa seminală	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.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
Denumiri alternative	HTE-CausalImpact, CATE causal impact, heterogeneous causal impact, subgroup causal impact analysis	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Înrudite	5	5
Rezumat	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.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.