In possibility theory, there are two kinds of possibilistic causal networks depending if possibilistic conditioning is based on the minimum or on the product operator. Similarly there are also two kinds of possibilistic logic: standard (min-based) possibilistic logic and quantitative (product-based) possibilistic logic. Recently, several equivalent transformations between standard possibilistic...

We are pleased to introduce a selection of the papers presented at the 1998 workshop on `Causal Networks from Inference to Data Mining', CaNew '98, [59]. This workshop was initiated from the feeling, shared by the organizers and co-chairs, that the ®eld of Bayesian and, in general, Causal Networks deserved special attention from the international research community. We had a growing feeling tha...

Discovery of causal relations from data is a fundamental objective of several scientific disciplines. Most causal discovery algorithms that use observational data can infer causality only up to a statistical equivalency class, thus leaving many causal relations undetermined. In general, complete identification of causal relations requires experimentation to augment discoveries from observationa...

Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death: Supplementary material MICHELLE SHARDELL∗,1, GREGORY E HICKS, LUIGI FERRUCCI Department of Epidemiology and Public Health, University of Maryland 660 West Redwood Street Baltimore, Maryland 21201, U.S.A. Department of Physical Therapy, University of Delaware 303 McKinly Lab Newark, Delawa...

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using aut...

Causal probabilistic networks provide a natural framework for representation of medical knowledge, allowing clinical experts to encode assumptions about causal dependencies between stochastic variables. Application inmedical decision support has produced promising results. However, model features and parameters may vary geoor demographically. Therefore methods are needed that allow for easy adj...

We derive bounds on the belief in a goal node given a set of acquired input nodes. The bounds apply to decomposable networks, a class of Bayesian Networks encompassing causal trees, causal polytrees, and some nonsingly connected networks. The difficulty of computing the bounds depends on the characteristics of the decomposable network. For directly connected networks with binary goal nodes, the...

Because of the high-speed and QOS guarantees, ATM networks are getting popular in multimedia applications. There are multimedia applications that require messages to be delivered in an order that preserves cause and effect relations in multicasts (called causal order multicast). Causal order multicast algorithms introduce an overhead because they append control information to messages to enforc...

This paper addresses a new problem concerning the evolution of influence relationships between communities in dynamic social networks. A weighted temporal multigraph is employed to represent the dynamics of the social networks and analyze the influence relationships between communities over time. To ensure the interpretability of the knowledge discovered, evolution of the influence relationship...