Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Robustne hägusa regressioonkatkestuse disain× | Kalduvusskoori sobitamine× | |
|---|---|---|
| Valdkond≠ | Põhjuslik järeldamine | Uurimisstatistika |
| Perekond≠ | Regression model | Process / pipeline |
| Tekkeaasta≠ | 2014 (robust CCT estimator); 2001 (fuzzy RDD formalization) | 1983 |
| Looja≠ | Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization) | Paul Rosenbaum and Donald Rubin |
| Tüüp≠ | Quasi-experimental causal inference with IV at threshold | Method |
| Algallikas≠ | Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326. 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 ↗ |
| Rööpnimetused≠ | Robust Fuzzy RDD, Fuzzy RD with robust inference, bias-corrected fuzzy RD, CCT fuzzy RDD | PSM, propensity score weighting, covariate balance |
| Seotud≠ | 5 | 3 |
| Kokkuvõte≠ | Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with a robust variance estimator, correcting the coverage failures of conventional bandwidth-optimal inference in both the sharp and fuzzy cases. | 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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