Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Two-Stage Least Squares (2SLS)× | Ulinganishaji wa Alama ya Mwelekeo× | |
|---|---|---|
| Nyanja≠ | Uhitimisho wa Kisababishi | Takwimu za Utafiti |
| Familia≠ | Regression model | Process / pipeline |
| Mwaka wa asili≠ | 2009 | 1983 |
| Mwanzilishi≠ | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) | Paul Rosenbaum and Donald Rubin |
| Aina≠ | Instrumental-variables regression | Method |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala≠ | instrumental variables, IV estimation, 2SLS, instrumental variable regression | PSM, propensity score weighting, covariate balance |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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