Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Metodes (CEM / Optimālā / Ģenētiskā)× | Propensity Score Matching× | |
|---|---|---|
| Nozare≠ | Cēloņsakarību secināšana | Pētniecības statistika |
| Saime≠ | Regression model | Process / pipeline |
| Izcelsmes gads≠ | 2012 | 1983 |
| Autors≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Paul Rosenbaum and Donald Rubin |
| Tips≠ | Matching for causal inference | Method |
| Pirmavots≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. 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 ↗ |
| Citi nosaukumi≠ | coarsened exact matching, optimal matching, genetic matching, CEM | PSM, propensity score weighting, covariate balance |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching. | 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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