Сравнение на методи
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| Регулиране на предната врата (критерий на предната врата)× | Причинно-следствена идентификация с насочени ациклични графи (do-calculus)× | Инструментални променливи чрез двуетапни най-малки квадрати (IV/2SLS)× | |
|---|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1995 | 2009 | 2009 |
| Създател≠ | Judea Pearl | Judea Pearl | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Тип≠ | Causal identification (graphical adjustment) | Causal identification framework | Instrumental-variables regression |
| Основополагащ източник≠ | Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688. DOI ↗ | 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≠ | frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment) | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Свързани≠ | 4 | 5 | 5 |
| Резюме≠ | 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. | 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). |
| ScholarGateНабор от данни ↗ |
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