Linganisha mbinu

	Muundo wa Utafiti wa Tukio la Athari Mbalimbali za Matibabu	Tofauti-katika-Tofauti (Diff-in-Diff)
Nyanja	Uhitimisho wa Kisababishi	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2021	1994
Mwanzilishi	Sun & Abraham (2021); Callaway & Sant'Anna (2021)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Aina	Quasi-experimental causal inference	Causal inference / panel 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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Majina mbadala	HTE event study, heterogeneous effects event study, group-time ATT event study, dynamic HTE design	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Zinazohusiana	3	5
Muhtasari	Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-way fixed-effects event studies — which assume a homogeneous effect — this approach explicitly models and recovers group-time average treatment effects (ATTs), addressing the contamination bias that arises when effects differ across treated units.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.