পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| বহু-সময়কালী অস্পষ্ট রিগ্রেশন ডিসকন্টিনিউটি ডিজাইন× | কারণগত অনুমানের জন্য ইনস্ট্রুমেন্টাল ভ্যারিয়েবলস (IV) পদ্ধতি× | |
|---|---|---|
| ক্ষেত্র≠ | কার্যকারণ অনুমান | স্বাস্থ্য অর্থনীতি |
| পরিবার≠ | Regression model | Process / pipeline |
| উদ্ভবের বছর≠ | 2001 (fuzzy RD); multi-period extension ~2010s | 1990s (modern applications) |
| প্রবর্তক≠ | Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| ধরন≠ | Quasi-experimental causal inference | Method |
| মৌলিক উৎস≠ | Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| অপর নাম | multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD | IV, two-stage least squares, TSLS, causal estimation |
| সম্পর্কিত≠ | 4 | 3 |
| সারসংক্ষেপ≠ | Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons. | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. |
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