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Compară metode

Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.

Echilibrarea Dinamică a Entropiei×Ponderarea Scorului de Propensitate (PSW / IPW)×
DomeniuInferență cauzalăInferență cauzală
FamilieRegression modelRegression model
Anul apariției2012-20181983 (propensity score); 2003 (efficient IPW estimator)
Autorul originalHainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologistsRosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
TipCausal inference / weighting estimatorCausal inference / reweighting
Sursa seminalăHainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46. 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 ↗
Denumiri alternativeDEB, longitudinal entropy balancing, entropy balancing with time-varying treatment, sequential entropy balancingPSW, inverse probability weighting, IPW, propensity-based weighting
Înrudite66
RezumatDynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes.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).
ScholarGateSet de date
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  1. v1
  2. 2 Surse
  3. PUBLISHED

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ScholarGateCompară metode: Dynamic Entropy Balancing · Propensity Score Weighting. Preluat la 2026-06-18 de pe https://scholargate.app/ro/compare