পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| বিষমজাতীয় চিকিৎসা প্রভাব এনট্রপি ব্যালেন্সিং× | প্রোপেনসিটি স্কোর ওয়েটিং (PSW / IPW)× | |
|---|---|---|
| ক্ষেত্র | কার্যকারণ অনুমান | কার্যকারণ অনুমান |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2012-2016 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| প্রবর্তক≠ | Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| ধরন≠ | Causal inference / heterogeneous effect estimation | Causal inference / reweighting |
| মৌলিক উৎস≠ | Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. 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 | HTE entropy balancing, CATE with entropy balancing, heterogeneous effects EB, subgroup entropy balancing | PSW, inverse probability weighting, IPW, propensity-based weighting |
| সম্পর্কিত≠ | 5 | 6 |
| সারসংক্ষেপ≠ | Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies across subgroups or individuals. It produces covariate-balanced weights without parametric propensity models, then uses those weights to estimate conditional average treatment effects (CATEs) across moderating variables. | 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 ↗ |
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