65 citations · 156 across the 21 of their papers we have counts for
26 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…
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…
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…
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…
Axiomatization of Interventional Probability Distributions
Kayvan Sadeghi, Terry Soo
Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizat…
Conditions and Assumptions for Constraint-based Causal Structure Learning
Kayvan Sadeghi, Terry Soo
We formalize constraint-based structure learning of the "true" causal graph from observed data when unobserved variables are also existent. We provide conditions for a "natural" fa…
On Finite Exchangeability and Conditional Independence
Kayvan Sadeghi
We study the independence structure of finitely exchangeable distributions over random vectors and random networks. In particular, we provide necessary and sufficient conditions fo…
Markov Properties of Discrete Determinantal Point Processes
Kayvan Sadeghi, Alessandro Rinaldo
Determinantal point processes (DPPs) are probabilistic models for repulsion. When used to represent the occurrence of random subsets of a finite base set, DPPs allow to model globa…
On Exchangeability in Network Models
Steffen L. Lauritzen, Alessandro Rinaldo, Kayvan Sadeghi
We derive representation theorems for exchangeable distributions on finite and infinite graphs using elementary arguments based on geometric and graph-theoretic concepts. Our resul…
Random Networks, Graphical Models, and Exchangeability
Steffen Lauritzen, Alessandro Rinaldo, Kayvan Sadeghi
We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical…
Faithfulness of Probability Distributions and Graphs
Kayvan Sadeghi
A main question in graphical models and causal inference is whether, given a probability distribution (which is usually an underlying distribution of data), there is a graph (o…
Unifying Markov Properties for Graphical Models
Steffen Lauritzen, Kayvan Sadeghi
Several types of graphs with different conditional independence interpretations --- also known as Markov properties --- have been proposed and used in graphical models. In this pap…
Hierarchical Models for Independence Structures of Networks
Kayvan Sadeghi, Alessandro Rinaldo
We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random…
Pairwise Markov properties for regression graphs
Kayvan Sadeghi, Nanny Wermuth
With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentratio…
On the Number of Non-zero Elements of Joint Degree Vectors
Eva Czabarka, Johannes Rauh, Kayvan Sadeghi +2
Joint degree vectors give the number of edges between vertices of degree and degree for in an -vertex graph. We find lower and upper bounds for the…
Total positivity in Markov structures
Shaun Fallat, Steffen Lauritzen, Kayvan Sadeghi +3
We discuss properties of distributions that are multivariate totally positive of order two (MTP2) related to conditional independence. In particular, we show that any independence…
Statistical Models for Degree Distributions of Networks
Kayvan Sadeghi, Alessandro Rinaldo
We define and study the statistical models in exponential family form whose sufficient statistics are the degree distributions and the bi-degree distributions of undirected labelle…
models for random hypergraphs with a given degree sequence
Despina Stasi, Kayvan Sadeghi, Alessandro Rinaldo +2
We introduce the beta model for random hypergraphs in order to represent the occurrence of multi-way interactions among agents in a social network. This model builds upon and gener…
Marginalization and Conditioning for LWF Chain Graphs
Kayvan Sadeghi
In this paper, we deal with the problem of marginalization over and conditioning on two disjoint subsets of the node set of chain graphs (CGs) with the LWF Markov property. For thi…
Markov Equivalences for Subclasses of Loopless Mixed Graphs
Kayvan Sadeghi
In this paper we discuss four problems regarding Markov equivalences for subclasses of loopless mixed graphs. We classify these four problems as finding conditions for internal Mar…
Stable mixed graphs
Kayvan Sadeghi
In this paper, we study classes of graphs with three types of edges that capture the modified independence structure of a directed acyclic graph (DAG) after marginalisation over un…