مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| روش کنترل ترکیبی تقویتشده با یادگیری ماشین× | روش تفاوت در تفاوت (Diff-in-Diff)× | |
|---|---|---|
| حوزه≠ | استنتاج علّی | اقتصادسنجی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2021 | 1994 |
| پدیدآور≠ | Ben-Michael, Feller & Rothstein | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| نوع≠ | Causal inference / quasi-experimental | Causal inference / panel regression |
| منبع بنیادین≠ | Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| نامهای دیگر≠ | ML-augmented SCM, augmented synthetic control, ASC, penalized synthetic control | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| مرتبط | 5 | 5 |
| خلاصه≠ | The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to model pre-treatment outcome trajectories. The augmentation corrects for residual imbalance left by the standard weighting step, yielding lower bias when no perfect synthetic control exists. | 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. |
| ScholarGateمجموعهداده ↗ |
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