Pr. Ioannis Tsamardinos is invited to give a talk at MPI for Intelligent Systems in Tuebingen.

Title: Advances in Integrative Causal Analysis

Abstract: We’ll present the concept and approach of Integrative Causal Analysis(INCA), where causal models and relations are induced from multiple,heterogeneous datasets. The datasets maybe heterogeneous in terms of the variable sets they measure or the experimental conditions (interventions)under which they were collected. To solve the problem, all causal models consistent with all datasets are induced and their invariant characteristics (causal features) are found. We introduce a technique where the problem is converted to a SAT (satisfiability) problem that exploits decades of research in satisfiability engines for improved efficiency. In addition, it is also shown that INCA algorithms can in principle also be extended to provide quantitative predictions. The general idea is tested on a couple of dozen real datasets where it is shown that the correlation between two variables never jointly measured, can be predicted based on causal models induced by 2 datasets with overlapping variables. The problem of resolving conflicts arising during learning is also dealt with as well as the problem of identifying the parts of the models that are of high confidence. Finally, new projects and directions are discussed and particular the application of these techniques to learning biological signal pathways from mass cytometry data.