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성향 점수 가중치 (PSW / IPW)×이중차분법 (Diff-in-Diff)×
분야인과추론계량경제학
계열Regression modelRegression model
기원 연도1983 (propensity score); 2003 (efficient IPW estimator)1994
창시자Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
유형Causal inference / reweightingCausal inference / panel regression
원전Rosenbaum, 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 ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
별칭PSW, inverse probability weighting, IPW, propensity-based weightingdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
관련65
요약Propensity 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).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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