Causal inference from statistical data [Elektronische Ressource] / von Xiaohai Sun

Causal Inference from Statistical Datazur Erlangung des akademischen Grades einesDoktors der Naturwissenschaftenvon der Fakultät für Informatikder Universität Fridericiana zu Karlsruhe (TH)genehmigteD i s s e r t a t i o nvonXiaohai Sunaus Shanghai, ChinaTag der mündlichen Prüfung: 15. April 2008Erster Gutachter: PD Dr. Dominik JanzingZweiter Gutachter: Prof. Dr. Bernhard SchölkopfAbstract“Automatic causal discovery” is a rather young research area, to which increasing atten-tion is paid in recent years as more and better data have become available. Until the earlynineties, most researchers still shunned away from discussing formal methods for infer-ring causal structure from purely observational statistical data without using controlled ex-periments, i.e., interventions. The seminal works of Spirtes, Glymour, and Scheines [153]and the works of Pearl [125] in the last fifteen years have established a promising basisof learning causality from such data. Bayesian networks are used as a concrete vehicle,where the corresponding directed acyclic graph can be interpreted causally. The test ofstatistical (conditional) independence between observed random variables provides a pri-mary tool for learning such causal graphs. The theory and the practical applications oftheir approach, however, are far from fully developed. The essential shortcomings are thefollowing.