Sammenlign metoder

	Bayesiansk følsomhedsanalyse for kausalitet	Propensity Score Matching
Fagområde	Kausal inferens	Forskningsstatistik
Familie	Regression model	Process / pipeline
Oprindelsesår	2000s–2010s	1983
Ophavsperson	McCandless, Gustafson & Austin (2007); Gustafson (2015)	Paul Rosenbaum and Donald Rubin
Type	Bayesian causal sensitivity analysis	Method
Oprindelig kilde	McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718.	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.
Aliasser	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	PSM, propensity score weighting, covariate balance
Relaterede	6	3
Resumé	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.