Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kikokotozi cha Kulinganisha chenye Nguvu× | Tofauti-katika-Tofauti Inayobadilika× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2010 | 2021 |
| Mwanzilishi≠ | Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998) | Callaway & Sant'Anna; Sun & Abraham |
| Aina≠ | Nonparametric causal inference / matching | Causal inference / quasi-experimental |
| Chanzo asilia≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Majina mbadala | dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Zinazohusiana≠ | 6 | 4 |
| Muhtasari≠ | The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
| ScholarGateSeti ya data ↗ |
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