Causal Models and Intelligent Data Management

Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods.
This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.

I. Causal Models.- 1. Statistics, Causality, and Graphs.- 2. Causal Conjecture.- 3. Who Needs Counterfactuals?.- 4. Causality: Independence and Determinism.- II. Intelligent Data Management.- 5. Intelligent Data Analysis and Deep Understanding.- 6. Learning Algorithms in High Dimensional Spaces.- 7. Learning Linear Causal Models by MML Sampling.- 8. Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis.- 9. On the Accuracy of Stochastic Complexity Approximations.- 10. AI Modelling for Data Quality Control Xiaohui Liu.- 11. New Directions in Text Categorization.