विधियों की तुलना करें

	स्थानिक द्वि-सुदृढ़ आकलन (Spatial Doubly Robust Estimation)	उपचार भारण की व्युत्क्रम प्रायिकता (IPW / IPTW)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2010s–2020s	2000
प्रवर्तक	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Robins, Hernán & Brumback
प्रकार	Semiparametric causal estimator	Causal inference weighting estimator
मौलिक स्रोत	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.
उपनाम	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
संबंधित	5	5
सारांश	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.