We adapt and use methods from the causal set approach to quantum gravity to analyse the structure of citation networks from academic papers on the arXiv, supreme court judgements from the US, and patents. We exploit the causal structure of of citation networks to measure the dimension of the Minkowski space in which these directed acyclic graphs can most easily be embedded explicitly taking tim...

Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting fro...

We consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to directed paths in a causal model. This type of knowledge naturally arises from experimental data, among others. Specifically, we consider the formalisms of Causal Bayesian Networks and Maximal Ancestral Graphs and their ...

We apply the principle of causal networks to develop a new tool for connectivity analysis in functional Magnetic Resonance Imaging (fMRI). The connections between active brain regions are modelled as causal relationships in a causal network. The causal networks are based on the notion of d-separation in a graph-theoretic context or, equivalently, on the notion of conditional independence in a s...

structures, 3accidentally correlated, 6adding-arrows, 36ancestrally, 31atomic states, 37 background knowledge, 26Bayesian network, 2Bayesian Networks Maximise En-tropy, 30 causal extension, 6causal irrelevance, 28causal Markov condition, 1, 3causal restriction, 8, 9conditional mutual information, 40constrained network, 40correlation restrictio...

Causal inference is one of the most fundamental reasoning processes and one that is essential for question-answering as well as more general AI applications such as decision-making and diagnosis. Bayesian Networks are a popular formalism for encoding (probabilistic) causal knowledge that allows for inference. We developed a system for acquiring causal knowledge from text. Our system identifies ...

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are “expression quantitative trait loci” or eQTLs for short) and presumably play a gene regulatory role, affecting the status of ...

In this paper we show that causal probabilistic models can facilitate design of robust and flexible fusion systems. Observed events resulting from stochastic causal processes can be modeled with the help of causal Bayesian networks, mathematically rigorous and compact probabilistic causal models. Bayesian networks explicitly represent conditional independence and this facilitates decentralized ...

Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs h...

We studied three potential causal mechanisms through which network dynamics of intergovernmental organizations (IGO) might cause convergence in domestic economic policies. First, IGO networks facilitate policy learning by providing relevant information. Second, they encourage policy emulation by creating a sense of affinity among countries that are closely connected by IGO networks. Finally, so...