Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Jutīguma analīze slēptai neobjektivitātei (Rozenbauma robežas / E-vērtība)× | Instrumentālās mainīgās, izmantojot divpakāpju mazāko kvadrātu metodi (IV/2SLS)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2002 | 2009 |
| Autors≠ | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Tips≠ | Sensitivity analysis for causal inference | Instrumental-variables regression |
| Pirmavots≠ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Citi nosaukumi | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). | 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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