विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| कार्यकारण खोज एल्गोरिदम (PC, FCI, LiNGAM)× | प्रोपेंसिटी स्कोर मैचिंग× | |
|---|---|---|
| क्षेत्र≠ | कारणात्मक अनुमान | अनुसंधान सांख्यिकी |
| परिवार≠ | Regression model | Process / pipeline |
| उद्भव वर्ष≠ | 2000 | 1983 |
| प्रवर्तक≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Paul Rosenbaum and Donald Rubin |
| प्रकार≠ | Causal structure learning | Method |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | PSM, propensity score weighting, covariate balance |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | 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. |
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