Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Алгоритми за причинно-следствено откриване (PC, FCI, LiNGAM)× | Причинно-следствена идентификация с насочени ациклични графи (do-calculus)× | Метод на разликите в разликите (Difference-in-Differences, DiD)× | |
|---|---|---|---|
| Област≠ | Причинно-следствено заключение | Причинно-следствено заключение | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2000 | 2009 | 1994 |
| Създател≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Judea Pearl | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Тип≠ | Causal structure learning | Causal identification framework | Causal inference / panel regression |
| Основополагащ източник≠ | 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 | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Други названия≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Свързани | 5 | 5 | 5 |
| 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. | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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