Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Ajustament per la porta d'entrada (Criteri de la porta d'entrada)× | Algorismes de descobriment causal (PC, FCI, LiNGAM)× | La identificació causal amb grafs acíclics dirigits (do-càlcul)× | |
|---|---|---|---|
| Camp | Inferència causal | Inferència causal | Inferència causal |
| Família | Regression model | Regression model | Regression model |
| Any d'origen≠ | 1995 | 2000 | 2009 |
| Autor original≠ | Judea Pearl | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Judea Pearl |
| Tipus≠ | Causal identification (graphical adjustment) | Causal structure learning | Causal identification framework |
| Font seminal≠ | 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 | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 |
| Àlies≠ | frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment) | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) |
| Relacionats≠ | 4 | 5 | 5 |
| Resum≠ | 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. | 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. |
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