Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the attack scenarios and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect attack scenarios in real-time. The proposed method is based on a causal approach due to the strength of causal methods in ...

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodol...

March 19, 2015 Version 2.0-10 Date 2015-03-18 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch Title Methods for Graphical Models and Causal Inference Description Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational ...

Predicting patient outcome is an important task in medical decision making, as clinician expectations of outcome drive testing and treatment decisions. Accurate models can assist clinicians by capitalizing on information from a broad spectrum of features to predict outcome. In an article in this journal, ‘A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-...

Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables (IVs) for causal inference. A basic idea is that IVs serve as an experimental handle, the turning of which may change each individual’s treatment status and, through and only through this effect, also change observed outcome. The average difference in observed outcome relati...

We present two algorithms for exact and ap­ proximate inference in causal networks. The first algorithm, dynamic conditioning, is a re­ finement of cutset conditioning that has lin­ ear complexity on some networks for which cutset conditioning is exponential. The sec­ ond algorithm, B-conditioning, is an algo­ rithm for approximate inference that allows one to trade-off the quality of approxima...

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This paper presents methods for modeling and assisting students’ understanding about causalities between physical quantities based on comparative reasoning. Our tutoring system discusses causalities in an object system with the student by choosing dialogue strategies according to student’s understanding states. The student’s understanding state is represented by the causal network and table. Di...