방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다기간 축소 정확 일치법× | 성향 점수 매칭× | |
|---|---|---|
| 분야≠ | 인과추론 | 연구 통계 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 2012–2021 | 1983 |
| 창시자≠ | Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings | Paul Rosenbaum and Donald Rubin |
| 유형≠ | Non-parametric matching / causal inference | Method |
| 원전≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24. 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 ↗ |
| 별칭≠ | Multi-period CEM, Longitudinal CEM, Panel CEM, Multi-wave CEM | PSM, propensity score weighting, covariate balance |
| 관련≠ | 6 | 3 |
| 요약≠ | Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant time periods, and then estimates a weighted average treatment effect that accounts for temporal structure. | 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. |
| ScholarGate데이터셋 ↗ |
|
|