Comparer des méthodes

	Échantillonnage pondéré en ligne	Pondération par score de propension (PSP / IPW)
Domaine	Méthodologie d'enquête	Inférence causale
Famille	Process / pipeline	Regression model
Année d'origine	Late 1990s–2000s	1983 (propensity score); 2003 (efficient IPW estimator)
Auteur d'origine	Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Type	Probability-adjusted online sampling technique	Causal inference / reweighting
Source fondatrice	Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley. ISBN: 978-1118456149	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.
Alias	web-based weighted sampling, internet survey weighting, online panel weighting, weighted internet sampling	PSW, inverse probability weighting, IPW, propensity-based weighting
Apparentées	4	6
Résumé	Online weighted sampling is the practice of recruiting respondents via internet platforms and then applying statistical weights to correct for unequal selection probabilities, coverage gaps, and differential non-response. It enables researchers to draw valid population inferences from web surveys by compensating for the structural biases inherent in online recruitment — including the fact that not all members of a target population have equal internet access or equal likelihood of joining a panel.	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).