Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Tofauti-katika-Tofauti (Diff-in-Diff)× | Ulinganishaji wa Alama ya Mwelekeo× | |
|---|---|---|
| Nyanja≠ | Ekonometriki | Takwimu za Utafiti |
| Familia≠ | Regression model | Process / pipeline |
| Mwaka wa asili≠ | 1994 | 1983 |
| Mwanzilishi≠ | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) | Paul Rosenbaum and Donald Rubin |
| Aina≠ | Causal inference / panel regression | Method |
| Chanzo asilia≠ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Majina mbadala | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) | PSM, propensity score weighting, covariate balance |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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