Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Kausaalinen mediatoiminnan analyysi (luonnollinen suora ja epäsuora vaikutus)× | Kausaalinen identifiointi suunnatuilla syklittömillä graafeilla (do-calculus)× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2010 | 2009 |
| Kehittäjä≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Judea Pearl |
| Tyyppi≠ | Counterfactual causal decomposition | Causal identification framework |
| Alkuperäislähde≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 |
| Rinnakkaisnimet≠ | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation. | 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. |
| ScholarGateAineisto ↗ |
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