পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মেশিন লার্নিং-অগমেন্টেড ইনভার্স প্রোবাবিলিটি ওয়েটিং (ML-IPW)× | প্রোপেনসিটি স্কোর ওয়েটিং (PSW / IPW)× | |
|---|---|---|
| ক্ষেত্র | কার্যকারণ অনুমান | কার্যকারণ অনুমান |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2003-2018 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| প্রবর্তক≠ | Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| ধরন≠ | Semiparametric causal estimator | Causal inference / reweighting |
| মৌলিক উৎস≠ | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI ↗ | 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 ↗ |
| অপর নাম | ML-IPW, nonparametric IPW, data-adaptive IPW, ML-augmented propensity weighting | PSW, inverse probability weighting, IPW, propensity-based weighting |
| সম্পর্কিত≠ | 5 | 6 |
| সারসংক্ষেপ≠ | Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting, ML-IPW controls for high-dimensional and nonlinear confounders that classical IPW misses, while retaining the intuitive weighting framework. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
| ScholarGateডেটাসেট ↗ |
|
|