Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ulinganifu wa Alama ya Tabia ya Athari za Matibabu kwa Vikundi Tofauti× | Tofauti-katika-Tofauti (Diff-in-Diff)× | |
|---|---|---|
| Nyanja≠ | Uhitimisho wa Kisababishi | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1983–2016 | 1994 |
| Mwanzilishi≠ | Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Aina≠ | Causal inference / matching with effect heterogeneity | Causal inference / panel regression |
| Chanzo asilia≠ | Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Majina mbadala≠ | HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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