A notably challenging problem related to event processing is recognizing the relations holding between events in a text, in particular temporal and causal relations. While there has been some research on temporal relations, the aspect of causality between events from a Natural Language Processing (NLP) perspective has hardly been touched. We propose an annotation scheme to cover different types...

Relativistic causality, namely, the impossibility of signaling at superluminal speeds, restricts the kinds of correlations which can occur between different parts of a composite physical system. Here we establish the basic restrictions which relativistic causality imposes on the joint probabilities involved in an experiment of the Einstein-PodolskyRosen-Bohm type. Quantum mechanics, on the othe...

This paper addresses questions about how the levels of causality (generic and single case causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is top-down, bottom-up, or the levels are independent. A second question is...

This paper introduces a kernel-based non-parametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event introduced by Hong, Liu, and Wang (2009). The main advantage of this test is its ability to examine a large number of lags, with higher-order lags discounted. In addition, our test ...

The current paradigm of vaccine pharmacovigilance includes three stages: signal detection, development of a causality hypothesis, and testing of the causality hypothesis [1]. Signal detection in the postmarketing setting largely relies on spontaneous reports of adverse events (AEs) following immunization (AEFI) and literature case reports. The development of a causality hypothesis involves an a...

We analyze the relations between Granger (G) non-causality and a notion of structural causality arising naturally from a general nonseparable recursive dynamic structural system. Building on classical notions of G non-causality, we introduce interesting and natural extensions, namely weak G non-causality and retrospective weakG non-causality. We show that structural non-causality and certain (r...

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series based on the estimated linear regression model and has been widely applied in economics and neuroscience due to its simplicity, understandability and easy implementation. Especially, its counterpart in frequency domain, spectral GC, has recently received growing attention to study causal inte...