Problem statement: Causality among events, more formally the causal ordering relation, is a powerful tool for analyzing and drawing inferences about distributed systems. The knowledge of the causal ordering relation between processes helps designers and the system itself solve a variety of problems in distributed systems. In distributed algorithms design, such knowledge helped ensure fairness a...

The sheer volume and complexity of publications in the biological sciences are straining traditional approaches to research planning. Nowhere is this problem more serious than in molecular and cellular cognition, since in this neuroscience field, researchers routinely use approaches and information from a variety of areas in neuroscience and other biology fields. Additionally, the multilevel in...

We consider an information-theoretic objective function for statistical modeling of time series that embodies a parametrized trade-off between the predictive power of a model and the model’s complexity. We study two distinct cases of optimal causal inference, which we call optimal causal filtering (OCF) and optimal causal estimation (OCE). OCF corresponds to the ideal case of having infinite da...

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estim...

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of Structural Causal Models, especially for cyclic models.

This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation con­ sistent with a set of background knowledge which explains all of the observed indepen­ dence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such