পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| শিক্ষা গবেষণায় প্রান্তিক কাঠামোগত মডেল× | ডিফারেন্স-ইন-ডিফারেন্সেস (ডিফ-ইন-ডিফ)× | |
|---|---|---|
| ক্ষেত্র≠ | কার্যকারণ অনুমান | অর্থমিতি |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2000 (method); 2006 (canonical education application) | 1994 |
| প্রবর্তক≠ | James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| ধরন≠ | Causal inference / weighted regression model | Causal inference / panel regression |
| মৌলিক উৎস≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| অপর নাম≠ | MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| সম্পর্কিত | 5 | 5 |
| সারসংক্ষেপ≠ | A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve. | 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. |
| ScholarGateডেটাসেট ↗ |
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