Causal effect identification considers whether an interventional probability distribution can
be uniquely determined from a passively observed distribution in a given causal structure. If …

Team semantics is a semantical framework for the study of dependence and independence
concepts ubiquitous in many areas such as databases and statistics. In recent works team …

Team semantics is a highly general framework for logics which describe dependencies and
independencies among variables. Typically, the (in) dependencies considered in this …

Bayesian networks are widely used to learn and reason about the dependence structure of
discrete variables. However, they can only formally encode symmetric conditional …

We define a variant of team semantics called multiteam semantics based on multisets and
study the properties of various logics in this framework. In particular, we define natural …

We introduce a novel variant of BSS machines called Separate Branching BSS machines (S-
BSS in short) and develop a Fagin-type logical characterisation for languages decidable in …

We study probabilistic team semantics which is a semantical framework allowing the study of
logical and probabilistic dependencies simultaneously. We examine and classify the …

Bayesian networks are a widely-used class of probabilistic graphical models capable of
representing symmetric conditional independence between variables of interest using the …

We study the expressivity and complexity of model checking linear temporal logic with team
semantics (TeamLTL). TeamLTL, despite being a purely modal logic, is capable of defining …

We classify the computational complexity of the satisfiability, validity, and model-checking
problems for propositional independence, inclusion, and team logic. Our main result shows …