Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Panel Data Inverse Probability Weighting× | Kadiriaji ya Uoanishaji wa Data za Paneli× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2000 | 1997-2021 |
| Mwanzilishi≠ | Robins, Hernan & Brumback | Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension |
| Aina≠ | Reweighting / causal inference | Quasi-experimental causal estimator |
| Chanzo asilia≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654. DOI ↗ |
| Majina mbadala | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW | panel matching, matching-on-panel-data, longitudinal matching estimator, PDME |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods. | The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable unit characteristics, estimating the average treatment effect on the treated (ATT) without requiring a parallel-trends assumption. |
| ScholarGateSeti ya data ↗ |
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