Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Подієве дослідження на панельних даних× | Зіставлення за показником схильності× | |
|---|---|---|
| Галузь≠ | Причинно-наслідковий висновок | Статистика досліджень |
| Родина≠ | Regression model | Process / pipeline |
| Рік появи≠ | 2021 | 1983 |
| Автор методу≠ | Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021) | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Causal inference / quasi-experimental panel design | Method |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | panel event study, event-study DiD, staggered event study, difference-in-differences event study | PSM, propensity score weighting, covariate balance |
| Пов'язані≠ | 6 | 3 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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