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| 베이즈 차이의 차이(Bayesian Difference-in-Differences)× | 동적 이중차분법 (Dynamic Difference-in-Differences)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2015-2023 | 2021 |
| 창시자≠ | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) | Callaway & Sant'Anna; Sun & Abraham |
| 유형≠ | Bayesian causal inference / panel regression | Causal inference / quasi-experimental |
| 원전≠ | Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| 별칭 | Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| 관련≠ | 5 | 4 |
| 요약≠ | Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available. | 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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