Belief net structure learning from uncertain interventions

By Daniel Eaton, Kevin P. Murphy. 2007

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. Yoshua Bengio
2. Anirudh Goyal
3. Hugo Larochelle
4. Nan Rosemary Ke
5. Simon Lacoste-Julien
6. Tristan Deleu
7. Stefan Bauer
8. Chris C. Emezue

Citations in the corpus

• Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Although a molecular intervention may be imperfect and affect multiple proteins (Eaton and Murphy, 2007b), we assume here that these interventions are perfect, and the intervention targets are known.”
• Learning Neural Causal Models from Unknown Interventions (2019, methodology): “[37] 5 17 2 2 8 2 16 Eaton and Murphy [10] 0 OOM 7 OOM OOM OOM OOM Proposed Method (SDI) 0 6 0 0 0 0 7”
• Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, methodology): “Eberhardt and Scheines (2007) and Eaton and Murphy (2007a) discuss how some other types of interventions can be modeled and used to learn Bayesian networks.”