ScholarGate
Msaidizi

Linganisha mbinu

Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.

Tathmini ya Sera Uzito wa Uwezekano wa Kinyume×Uzito wa Alama ya Mwelekeo (PSW / IPW)×
NyanjaUhitimisho wa KisababishiUhitimisho wa Kisababishi
FamiliaRegression modelRegression model
Mwaka wa asili1952 (IPW origin); 2000s (policy evaluation application)1983 (propensity score); 2003 (efficient IPW estimator)
MwanzilishiHorvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
AinaReweighting estimator for causal policy analysisCausal inference / reweighting
Chanzo asiliaImbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. 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 ↗
Majina mbadalaIPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weightingPSW, inverse probability weighting, IPW, propensity-based weighting
Zinazohusiana66
MuhtasariPolicy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).
ScholarGateSeti ya data
  1. v1
  2. 2 Vyanzo
  3. PUBLISHED
  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

Nenda kwenye utafutaji Pakua slaidi

ScholarGateLinganisha mbinu: Policy Evaluation Inverse Probability Weighting · Propensity Score Weighting. Imepatikana 2026-06-19 kutoka https://scholargate.app/sw/compare