Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Utafiti wa Tukio la Athari Mbalimbali za Matibabu× | Tofauti-katika-Tofauti Inayobadilika× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili | 2021 | 2021 |
| Mwanzilishi≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Aina≠ | Quasi-experimental causal inference | Causal inference / quasi-experimental |
| 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. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Majina mbadala | HTE event study, heterogeneous effects event study, group-time ATT event study, dynamic HTE design | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Zinazohusiana≠ | 3 | 4 |
| 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. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
| ScholarGateSeti ya data ↗ |
|
|