পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| নীতি মূল্যায়নে ইন্সট্রুমেন্টাল ভেরিয়েবল (Instrumental Variables) ব্যবহার× | প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)× | |
|---|---|---|
| ক্ষেত্র≠ | কার্যকারণ অনুমান | গবেষণা পরিসংখ্যান |
| পরিবার≠ | Regression model | Process / pipeline |
| উদ্ভবের বছর≠ | 1996 (modern policy-evaluation framing); IV roots 1920s | 1983 |
| প্রবর্তক≠ | Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953) | Paul Rosenbaum and Donald Rubin |
| ধরন≠ | Quasi-experimental causal inference / IV regression | Method |
| মৌলিক উৎস≠ | Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455. 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≠ | IV policy evaluation, 2SLS policy analysis, natural-experiment IV, policy IV estimation | PSM, propensity score weighting, covariate balance |
| সম্পর্কিত≠ | 5 | 3 |
| সারসংক্ষেপ≠ | Instrumental Variables (IV) estimation for policy evaluation is a quasi-experimental technique that uses an exogenous instrument — a variable that shifts exposure to a policy but is otherwise unrelated to the outcome — to recover the causal effect of a program or intervention from non-experimental data. Popularised in policy research by Angrist, Imbens, and Rubin (1996), it identifies the Local Average Treatment Effect (LATE) among units whose treatment status is changed by the instrument. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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