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| Algorytmy odkrywania przyczynowości (PC, FCI, LiNGAM)× | DAG Causal Identification× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|---|
| Dziedzina≠ | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe | Ekonometria |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 2000 | 2009 | 2019 |
| Twórca≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Judea Pearl | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Causal structure learning | Causal identification framework | Linear regression |
| Źródło pierwotne≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne | 5 | 5 | 5 |
| Podsumowanie≠ | 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. | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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