Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Instrumentālās mainīgās, izmantojot divpakāpju mazāko kvadrātu metodi (IV/2SLS)× | Propensity Score Matching× | |
|---|---|---|
| Nozare≠ | Cēloņsakarību secināšana | Pētniecības statistika |
| Saime≠ | Regression model | Process / pipeline |
| Izcelsmes gads≠ | 2009 | 1983 |
| Autors≠ | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) | Paul Rosenbaum and Donald Rubin |
| Tips≠ | Instrumental-variables regression | Method |
| Pirmavots≠ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 | 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≠ | instrumental variables, IV estimation, 2SLS, instrumental variable regression | PSM, propensity score weighting, covariate balance |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). | 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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