Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļdatu marginālais strukturālais modelis (MSM)× | Panel Data Difference-in-Differences (Panel DiD / TWFE)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2000 | 1985–2004 |
| Autors≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004) |
| Tips≠ | Causal model for time-varying treatments | Causal inference / panel regression |
| Pirmavots≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Citi nosaukumi | MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM | Two-Way Fixed Effects DiD, TWFE, Panel DiD, Panel Diff-in-Diff |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conventional regression cannot handle. | Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units. |
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