Pages that link to "Item:Q6047797"
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The following pages link to Bayesian inference of causal effects from observational data in Gaussian graphical models (Q6047797):
Displaying 12 items.
- Structural learning and estimation of joint causal effects among network-dependent variables (Q113084) (← links)
- Estimating high-dimensional intervention effects from observational data (Q1043733) (← links)
- Rejoinder to the discussion of ``Bayesian graphical models for modern biological applications'' (Q2152189) (← links)
- Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies (Q2226709) (← links)
- Jointly Interventional and Observational Data: Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Q5379910) (← links)
- A Bayesian view of doubly robust causal inference: Table 1. (Q5384401) (← links)
- Causal Graphical Models with Latent Variables: Learning and Inference (Q5900906) (← links)
- Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo (Q5970825) (← links)
- Bayesian causal inference in probit graphical models (Q6198362) (← links)
- Bayesian sample size determination for causal discovery (Q6577815) (← links)
- Individualized causal discovery with latent trajectory embedded Bayesian networks (Q6589265) (← links)
- Bayesian graphical modeling for heterogeneous causal effects (Q6629904) (← links)
