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| Aggiustamento Frontdoor (Criterio Frontdoor)× | Algoritmi di Scoperta Causale (PC, FCI, LiNGAM)× | Variabili Strumentali tramite Minimi Quadrati a Due Stadi (IV/2SLS)× | |
|---|---|---|---|
| Campo | Inferenza causale | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 1995 | 2000 | 2009 |
| Ideatore≠ | Judea Pearl | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Tipo≠ | Causal identification (graphical adjustment) | Causal structure learning | Instrumental-variables regression |
| Fonte seminale≠ | Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688. DOI ↗ | 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 |
| Alias≠ | frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment) | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Correlati≠ | 4 | 5 | 5 |
| Sintesi≠ | Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured. | 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. | 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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