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| 空間的逆確率重み付け（Spatial IPW）× | 差分の差 (Difference-in-Differences, DiD)× | |
|---|---|---|
| 分野≠ | 因果推論 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2010s | 1994 |
| 提唱者≠ | Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| 種類≠ | Quasi-experimental / causal inference | Causal inference / panel regression |
| 原典≠ | 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 ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 別名≠ | Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| 関連≠ | 6 | 5 |
| 概要≠ | 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. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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