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Spatial Doubly Robust Estimation/Evidence
Method evidence record

Spatial Doubly Robust Estimation

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.

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Spatial Doubly Robust Causal Estimation
Taxonomic method record · regression-model / causal-inference
  • 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. · DOI 10.1111/biom.13049
  • Kennedy, E. H. (2016). Semiparametric theory and empirical processes in causal inference. In H. He, P. Wu, & D.-G. Chen (Eds.), Statistical Causal Inferences and Their Applications in Public Health Research (pp. 141-167). Springer. · URL
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Related methods

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Same method familyDifference-in-Differencesmachine-suggested · Relational suggestion, not evidence.Same method familyDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.Same method familyGeographically Weighted Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyInverse Probability Weightingmachine-suggested · Relational suggestion, not evidence.See alsoPropensity Score Matchingmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Sources

2 recorded citations, copied from the method source record.

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