4 papers
Interpretable Causal Graphical Models for Equilibrium Systems with Confounding
Kai Z. Teh, Kayvan Sadeghi, Terry Soo
In applications, quantities of interest are often modelled in equilibrium or an equilibrium solution is sought. The presence of confounding makes causal inference in this setting c…
A General Framework on Conditions for Constraint-based Causal Learning
Kai Z. Teh, Kayvan Sadeghi, Terry Soo
Most constraint-based causal learning algorithms provably return the correct causal graph under certain correctness conditions, such as faithfulness. By representing any constraint…
Towards Robust Causal Effect Identification Beyond Markov Equivalence
Kai Z. Teh, Kayvan Sadeghi, Terry Soo
Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying caus…
Localised Natural Causal Learning Algorithms for Weak Consistency Conditions
Kai Z Teh, Kayvan Sadeghi, Terry Soo
By relaxing conditions for natural structure learning algorithms, a family of constraint-based algorithms containing all exact structure learning algorithms under the faithfulness…