Comparer des méthodes

	Analyse de sensibilité bayésienne pour la causalité	Différences-en-différences bayésiennes
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	2015-2023
Auteur d'origine	McCandless, Gustafson & Austin (2007); Gustafson (2015)	Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)
Type	Bayesian causal sensitivity analysis	Bayesian causal inference / panel regression
Source fondatrice	McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718.	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.
Alias	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator
Apparentées	6	5
Résumé	Bayesian sensitivity analysis for causality quantifies how much an unmeasured confounder would need to influence both treatment assignment and outcome to overturn a causal conclusion. Rather than testing a single worst-case scenario, it places prior distributions over the strength of hidden confounding, propagates uncertainty through a full Bayesian model, and reports a posterior distribution for the causal effect that honestly reflects what is and is not identified from observed data.	Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available.