পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| স্থানিক প্রান্তিক কাঠামোগত মডেল× | স্থানিক ইন্সট্রুমেন্টাল ভ্যারিয়েবল (Spatial IV / Spatial 2SLS)× | |
|---|---|---|
| ক্ষেত্র | কার্যকারণ অনুমান | কার্যকারণ অনুমান |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 2000s–2010s | 1988-1998 |
| প্রবর্তক≠ | Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature | Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework) |
| ধরন≠ | Causal inference / spatial weighting | Quasi-experimental causal inference with spatial dependence |
| মৌলিক উৎস≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Kelejian, H. H., & Prucha, I. R. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics, 17(1), 99-121. DOI ↗ |
| অপর নাম | Spatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural model | Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV |
| সম্পর্কিত | 6 | 6 |
| সারসংক্ষেপ≠ | The Spatial Marginal Structural Model (Spatial MSM) extends the classical marginal structural model to settings where units are geographically distributed and spatial dependencies — such as neighborhood spillovers, clustering, and spatial confounding — may bias causal estimates. It estimates causal effects of spatially varying exposures by constructing inverse probability weights that account for both individual covariates and spatial location, then fitting a weighted outcome model in the resulting pseudo-population. | Spatial Instrumental Variables (Spatial IV) is a causal inference method for settings where units — regions, firms, neighborhoods — are spatially interdependent, creating endogeneity that standard IV approaches ignore. It constructs instruments from the spatially lagged values of exogenous characteristics of neighboring units, then applies two-stage least squares to recover unbiased causal estimates in the presence of both endogenous regressors and spatial autocorrelation. |
| ScholarGateডেটাসেট ↗ |
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