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Poliitikahindamise kalduvuskoormise kaalutamine×Erinevused erinevustes (Diff-in-Diff)×
ValdkondPõhjuslik järeldamineÖkonomeetria
PerekondRegression modelRegression model
Tekkeaasta1983/20031994
LoojaRosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TüüpQuasi-experimental causal inferenceCausal inference / panel regression
AlgallikasHirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
RööpnimetusedPSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSWdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Seotud65
KokkuvõtePolicy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization.Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.
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ScholarGateVõrdle meetodeid: Policy Evaluation Propensity Score Weighting · Difference-in-Differences. Loetud 2026-06-17 aadressilt https://scholargate.app/et/compare