6 papers
Characterizing and Identifying Separable Graphical Models
Christopher Meek, Kayvan Sadeghi
We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirected edges, that are capable of…
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…
On Exponential Random Graph Models with Dyadic Independence
Kayvan Sadeghi
We show that the only exponential random graph model with n nodal parameters, dyads being independent, and the natural assumption of permutation-equivariant nodal parametrization i…
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…
Causal Models for Growing Networks
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Kayvan Sadeghi
Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the \textit{distribution} of edge…