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。
| 政策イベントスタディ（パネルデータ）× | 傾向スコアマッチング× | |
|---|---|---|
| 分野≠ | 因果推論 | 研究統計 |
| 系統≠ | Regression model | Process / pipeline |
| 提唱年≠ | 2021 | 1983 |
| 提唱者≠ | Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021) | Paul Rosenbaum and Donald Rubin |
| 種類≠ | Causal inference / quasi-experimental panel design | Method |
| 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 ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| 別名≠ | panel event study, event-study DiD, staggered event study, difference-in-differences event study | PSM, propensity score weighting, covariate balance |
| 関連≠ | 6 | 3 |
| 概要≠ | A panel event study is a quasi-experimental design that traces how an outcome evolves in periods before and after a policy event, using unit and time fixed effects to identify the causal effect. Widely used in economics and policy research, it tests for anticipation effects, verifies parallel pre-trends, and estimates dynamic treatment effects across post-treatment horizons — making it the standard toolkit for rigorous policy evaluation with observational panel data. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
| ScholarGateデータセット ↗ |
|
|