পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| কার্যকারণ আবিষ্কার অ্যালগরিদম (PC, FCI, LiNGAM)× | ডিফারেন্স-ইন-ডিফারেন্সেস (ডিফ-ইন-ডিফ)× | সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন× | |
|---|---|---|---|
| ক্ষেত্র≠ | কার্যকারণ অনুমান | অর্থমিতি | অর্থমিতি |
| পরিবার | Regression model | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2000 | 1994 | 2019 |
| প্রবর্তক≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) | Wooldridge (textbook treatment); classical least squares |
| ধরন≠ | Causal structure learning | Causal inference / panel regression | Linear regression |
| মৌলিক উৎস≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| অপর নাম≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| সম্পর্কিত | 5 | 5 | 5 |
| সারসংক্ষেপ≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateডেটাসেট ↗ |
|
|
|