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| Paikallinen kausaalivaikutusanalyysi× | Propensity Score Matching× | |
|---|---|---|
| Tieteenala≠ | Kausaalipäättely | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe≠ | Regression model | Process / pipeline |
| Syntyvuosi≠ | 2010s (codified) | 1983 |
| Kehittäjä≠ | Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988) | Paul Rosenbaum and Donald Rubin |
| Tyyppi≠ | Quasi-experimental causal inference with spatial data | Method |
| Alkuperäislähde≠ | Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126. 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 ↗ |
| Rinnakkaisnimet≠ | spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation | PSM, propensity score weighting, covariate balance |
| Liittyvät≠ | 4 | 3 |
| Tiivistelmä≠ | Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas. | 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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