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| Αλγόριθμοι Αιτιακής Ανακάλυψης (PC, FCI, LiNGAM)× | Αντιστοίχιση Βαθμολογίας Προδιάθεσης× | |
|---|---|---|
| Πεδίο≠ | Αιτιακή Συμπερασματολογία | Ερευνητική Στατιστική |
| Οικογένεια≠ | Regression model | Process / pipeline |
| Έτος προέλευσης≠ | 2000 | 1983 |
| Δημιουργός≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Paul Rosenbaum and Donald Rubin |
| Τύπος≠ | Causal structure learning | Method |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | PSM, propensity score weighting, covariate balance |
| Συναφείς≠ | 5 | 3 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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