পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| বহু-সময়কাল কার্যকারণ প্রভাব বিশ্লেষণ× | ব্যাহত সময় সিরিজ (Interrupted Time Series - ITS) বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র | কার্যকারণ অনুমান | কার্যকারণ অনুমান |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2015 (base); multi-period extensions 2017–present | 2002 |
| প্রবর্তক≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| ধরন≠ | Bayesian structural time-series / quasi-experimental | Quasi-experimental segmented regression |
| মৌলিক উৎস≠ | Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI ↗ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| অপর নাম≠ | multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| সম্পর্কিত≠ | 6 | 5 |
| সারসংক্ষেপ≠ | Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects. | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
| ScholarGateডেটাসেট ↗ |
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