পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মেশিন লার্নিং-অগমেন্টেড প্যানেল ইভেন্ট স্টাডি× | ডিফারেন্স-ইন-ডিফারেন্সেস (ডিফ-ইন-ডিফ)× | |
|---|---|---|
| ক্ষেত্র≠ | কার্যকারণ অনুমান | অর্থমিতি |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2019-2021 | 1994 |
| প্রবর্তক≠ | Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| ধরন≠ | Causal inference / quasi-experimental | Causal inference / panel regression |
| মৌলিক উৎস≠ | Chernozhukov, V., Wuthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association, 116(536), 1849-1864. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| অপর নাম≠ | ML-augmented event study, ML event study, panel event study with ML, machine learning event study | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| সম্পর্কিত≠ | 3 | 5 |
| সারসংক্ষেপ≠ | The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel trends, and produce valid causal effect estimates across multiple post-event periods. | 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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