The ultimate aim of this investigation is to develop computer systems capable of operating autonomously in dynamic and uncertain environments. The investigation comprises theoretical and experimental studies aiming at fusing causal and counterfactual relationships on top of probabilistic information to improve tasks of diagnosis, situation assessment and decision making in data-intensive applications. Specific research issues to investigate include developing algorithms and conceptual tools for diagnosis and situation assessment, using causal and counterfactual relationships, and developing novel methods of structure learning using propensity-score estimation and C-equivalence tests, with applications to epidemiology and molecular biology.
The theoretical part of this research touches on the core of human knowledge and scientific inquiry, and is having profound methodological ramifications in the fields of epidemiology, social science, economics, medicine and biology, where causal knowledge plays a major role. The practical part focuses on the development of new algorithms for causal and counterfactual reasoning, decision making and structure learning, with direct applications in areas that use graphical methods to encode knowledge.
