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| Spatial Inverse Probability Weighting (Spatial IPW)× | Propensity Score Matching× | |
|---|---|---|
| Fagområde≠ | Kausal inferens | Forskningsstatistik |
| Familie≠ | Regression model | Process / pipeline |
| Oprindelsesår≠ | 2010s | 1983 |
| Ophavsperson≠ | Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019) | Paul Rosenbaum and Donald Rubin |
| Type≠ | Quasi-experimental / causal inference | Method |
| Oprindelig kilde≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. DOI ↗ | 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. DOI ↗ |
| Aliasser≠ | Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW | PSM, propensity score weighting, covariate balance |
| Relaterede≠ | 6 | 3 |
| Resumé≠ | Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data. | 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. |
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