Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Оцінка політики за допомогою зіставлення за показником схильності× | Зіставлення за показником схильності× | |
|---|---|---|
| Галузь≠ | Причинно-наслідковий висновок | Статистика досліджень |
| Родина≠ | Regression model | Process / pipeline |
| Рік появи≠ | 1983; policy evaluation adaptation 1997 | 1983 |
| Автор методу≠ | Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Quasi-experimental matching estimator | Method |
| Основоположне джерело≠ | 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 ↗ | 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≠ | PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation | PSM, propensity score weighting, covariate balance |
| Пов'язані≠ | 6 | 3 |
| Підсумок≠ | Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment. | 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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