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| Kauzális feltáró algoritmusok (PC, FCI, LiNGAM)× | A különbség-különbségek (Diff-in-Diff) módszer× | |
|---|---|---|
| Tudományterület≠ | Oksági következtetés | Ökonometria |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2000 | 1994 |
| Megalkotó≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Típus≠ | Causal structure learning | Causal inference / panel regression |
| Alapmű≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Alternatív nevek≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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