Final Report Summary - CAUSALHIGHDIM (Causal Statistical Inference from High-Dimensional Data)
This project's goal was to make causal inference techniques that are based on additive noise models applicable to high-dimensional data sets and to apply them to biological data sets. We furthermore planned to develop new methodology for a setting in which the system is observed in different environments.
We were able to achieve all of these goals (detailed results can be found under "3. WORK PROGRESS AND ACHIEVEMENTS DURING THE PERIOD"). The interest in causal inference techniques is increasing both in the community of machine learning and statistics. We therefore believe that our results have the potential of developing major impact. The method of invariant prediction may lead to new models for transfer learning, multitask learning and instrumental variables.
We were able to achieve all of these goals (detailed results can be found under "3. WORK PROGRESS AND ACHIEVEMENTS DURING THE PERIOD"). The interest in causal inference techniques is increasing both in the community of machine learning and statistics. We therefore believe that our results have the potential of developing major impact. The method of invariant prediction may lead to new models for transfer learning, multitask learning and instrumental variables.