Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Алгоритми за причинно-следствено откриване (PC, FCI, LiNGAM)× | Съгласуване по показател на склонност× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Статистика за изследвания |
| Семейство≠ | Regression model | Process / pipeline |
| Година на възникване≠ | 2000 | 1983 |
| Създател≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Causal structure learning | Method |
| Основополагащ източник≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | 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≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | PSM, propensity score weighting, covariate balance |
| Свързани≠ | 5 | 3 |
| Резюме≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | 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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