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	空間的二重頑健推定量	傾向スコアマッチング
分野	因果推論	研究統計
系統	Regression model	Process / pipeline
提唱年	2010s–2020s	1983
提唱者	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Paul Rosenbaum and Donald Rubin
種類	Semiparametric causal estimator	Method
原典	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
別名	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	PSM, propensity score weighting, covariate balance
関連	5	3
概要	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.