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Regression modelQuasi-experimental / causal inference

Uthabiti wa Kina wa Angani (Spatial Doubly Robust Estimation)

Uthabiti wa kina wa angani ni mbinu ya kuingilia kati ya kimahesabu inayochanganya uzani wa alama ya tabia (propensity score weighting) na uundaji wa marejesho ya matokeo (outcome regression modeling) — ikitoa ulinzi dhidi ya kutofafanuliwa vibaya kwa mojawapo ya vipengele — huku ikizingatia uhusiano wa anga kati ya vitengo. Inapanua kiwango cha kawaida cha kuongeza uzani wa kinyume cha uwezekano (augmented inverse probability weighting - AIPW) kwa mazingira ambapo mgao wa matibabu na matokeo yamepangwa kwa kijiografia au hutegemea anga.

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Vyanzo

  1. 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
  2. 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. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Spatial Doubly Robust Causal Estimation. ScholarGate. https://scholargate.app/sw/causal-inference/spatial-doubly-robust-estimation

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Imerejelewa na

ScholarGateSpatial Doubly Robust Estimation (Spatial Doubly Robust Causal Estimation). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/causal-inference/spatial-doubly-robust-estimation · Seti ya data: https://doi.org/10.5281/zenodo.20539026