Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Identificação Causal com Grafos Acíclicos Direcionados (cálculo-do)× | Variáveis Instrumentais via Mínimos Quadrados em Dois Estágios (IV/2SLS)× | |
|---|---|---|
| Área | Inferência causal | Inferência causal |
| Família | Regression model | Regression model |
| Ano de origem | 2009 | 2009 |
| Autor original≠ | Judea Pearl | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Tipo≠ | Causal identification framework | Instrumental-variables regression |
| Fonte seminal≠ | 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 |
| Outros nomes≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Relacionados | 5 | 5 |
| Resumo≠ | 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). |
| ScholarGateConjunto de dados ↗ |
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