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| Đánh giá Tác động Phản thực Đa kỳ× | Khác biệt trong Khác biệt Động× | |
|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2000s–2010s | 2021 |
| Người khởi xướng≠ | Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians | Callaway & Sant'Anna; Sun & Abraham |
| Loại≠ | Causal inference / quasi-experimental evaluation | Causal inference / quasi-experimental |
| Công trình gốc≠ | Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Tên gọi khác | multi-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIE | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Liên quan | 4 | 4 |
| Tóm tắt≠ | Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss. | 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. |
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