Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Kausalidentifitseerimine suunatud atsükliliste graafide abil (do-arvutus)× | Instrumentaalmuutujad kaheastmelise vähimruutude meetodi abil (IV/2SLS)× | |
|---|---|---|
| Valdkond | Põhjuslik järeldamine | Põhjuslik järeldamine |
| Perekond | Regression model | Regression model |
| Tekkeaasta | 2009 | 2009 |
| Looja≠ | Judea Pearl | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Tüüp≠ | Causal identification framework | Instrumental-variables regression |
| Algallikas≠ | 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 |
| Rööpnimetused≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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). |
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