Linganisha mbinu

	Muundo wa Utafiti wa Tukio (Utafiti wa Tukio wa Kiusababishi)	Uchanganuzi wa Mfululizo wa Wakati Uliokatizwa (ITS)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2021	2002
Mwanzilishi	Sun & Abraham (2021); Callaway & Sant'Anna (2021)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Aina	Dynamic causal panel regression	Quasi-experimental segmented regression
Chanzo asilia	Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199.	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.
Majina mbadala	dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Zinazohusiana	5	5
Muhtasari	The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021).	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.