手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ロバスト差分-イン-差分法× | 異質的処置効果差の差 (HTE-DiD)× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2021-2023 | 2021 |
| 提唱者≠ | Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023) | Callaway & Sant'Anna; Sun & Abraham |
| 種類 | Causal inference / panel regression | Causal inference / panel regression |
| 原典 | 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 ↗ |
| 別名 | robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD | HTE-DiD, heterogeneous DiD, CATT estimator, group-time ATT |
| 関連≠ | 5 | 4 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
|
|