เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Robust Difference-in-Differences× | Heterogeneous Treatment Effect Difference-in-Differences (HTE-DiD)× | |
|---|---|---|
| สาขาวิชา | การอนุมานเชิงสาเหตุ | การอนุมานเชิงสาเหตุ |
| ตระกูล | Regression model | Regression model |
| ปีกำเนิด≠ | 2021-2023 | 2021 |
| ผู้ริเริ่ม≠ | Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023) | Callaway & Sant'Anna; Sun & Abraham |
| ประเภท | Causal inference / panel regression | Causal inference / panel regression |
| แหล่งต้นตำรับ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| ชื่อเรียกอื่น | robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD | HTE-DiD, heterogeneous DiD, CATT estimator, group-time ATT |
| ที่เกี่ยวข้อง≠ | 5 | 4 |
| สรุป≠ | Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate group-time average treatment effects (ATTs) separately and then aggregate them in a theoretically sound way. | HTE-DiD extends the classic Difference-in-Differences estimator to settings where treatment effects vary across units, time periods, or treatment cohorts. Developed formally by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it avoids the biases that arise when a conventional two-way fixed-effects regression is used with staggered adoption or effect heterogeneity, by estimating cohort-and-time-specific average treatment effects that can then be aggregated flexibly. |
| ScholarGateชุดข้อมูล ↗ |
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