Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Algoritmy pro objevování kauzality (PC, FCI, LiNGAM)× | Kauzalní identifikace pomocí orientovaných acyklických grafů (do-calculus)× | Instrumentální proměnné pomocí dvoufázové metody nejmenších čtverců (IV/2SLS)× | |
|---|---|---|---|
| Obor | Kauzální inference | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model | Regression model |
| Rok vzniku≠ | 2000 | 2009 | 2009 |
| Tvůrce≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Judea Pearl | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Typ≠ | Causal structure learning | Causal identification framework | Instrumental-variables regression |
| Původní zdroj≠ | 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 |
| Další názvy≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Příbuzné | 5 | 5 | 5 |
| Shrnutí≠ | 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. | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). |
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