We introduce a measure of causality that captures the functional dependencies in dynamical
systems and subsequently, define anew type of graphical model, functional dependency …

Causal discovery, the inference of causal relations among variables from data, is a
fundamental problem of science. Nowadays, due to an increased awareness of data privacy …

In recent years there has been a growing interest in developing communication systems that
are able to deliver messages respecting potential causality. Unfortunately, causal delivery …

Causal DAGs (also known as Bayesian networks) are a popular tool for encoding
conditional dependencies between random variables. In a causal DAG, the random …

In a distributed system, high-level actions can be modeled by nonatomic events. This paper
proposes causality relations between distributed nonatomic events and provides efficient …

In a Distributed System with N sites, the precise detection of causal relationships between
events can only be done with vector clocks of size N. This gives rise to scalability and …

Distributed systems have complex designs which are difficult to understand and verify. A
rigorous specification of such systems using mathematical techniques such as formal …

The vector clock is a fundamental tool for tracking causality in distributed applications.
Unfortunately, it does not scale well to large systems because each process needs to …

A causal ordering protocol ensures that if two messages are causally related and have the
same destination, they are delivered to the application in their sending order. Causal order …

Casual message-logging protocols have several attractive properties: they introduce no
blocking, send no additional messages over those sent by the application, and never create …