Bayesian Sensitivity Analysis for Causality
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.
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Method map
The neighbourhood of related methods — select a node to explore.
Allikad
- McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718. DOI: 10.1002/sim.3460 ↗
- Gustafson, P. (2015). Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data. CRC Press / Chapman & Hall. ISBN: 9781439869390
Kuidas sellele lehele viidata
ScholarGate. (2026, June 3). Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Inference. ScholarGate. https://scholargate.app/et/causal-inference/bayesian-sensitivity-analysis-for-causality
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bayes'i erinevus-erinevustesPõhjuslik järeldamine↔ compare
- Topeltrobustne hindamine (AIPW)Põhjuslik järeldamine↔ compare
- Instrumentaalmuutujate (IV) meetod kausaalse järelduse tegemiseksTerviseökonoomika↔ compare
- Marginaalne strukturaalne mudel (MSM)Põhjuslik järeldamine↔ compare
- Kalduvusskoori sobitamineUurimisstatistika↔ compare
- Tundlikkusanalüüs põhjuslikkuse jaoksPõhjuslik järeldamine↔ compare
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