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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Хетерогенно съвпадане с втвърдени точни съвпадения на ефекта от лечението× | Съгласуване по показател на склонност× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Статистика за изследвания |
| Семейство≠ | Regression model | Process / pipeline |
| Година на възникване≠ | 2012-2013 | 1983 |
| Създател≠ | Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Matching-based causal inference with subgroup CATE estimation | Method |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity | PSM, propensity score weighting, covariate balance |
| Свързани≠ | 5 | 3 |
| Резюме≠ | Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each bin, conditional average treatment effects (CATEs) are computed within or across these strata, revealing where treatment works, for whom, and by how much. | 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. |
| ScholarGateНабор от данни ↗ |
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