পদ্ধতির তুলনা করুন

	স্থানিক দ্বিগুণ শক্তিশালী প্রাক্কলন	ইনভার্স প্রোবাবিলিটি অফ ট্রিটমেন্ট ওয়েটিং (IPW / IPTW)
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2010s–2020s	2000
প্রবর্তক	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Robins, Hernán & Brumback
ধরন	Semiparametric causal estimator	Causal inference weighting estimator
মৌলিক উৎস	Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
অপর নাম	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
সম্পর্কিত	5	5
সারসংক্ষেপ	Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.