Knowledge in the form “A causes B” and “A does not cause B” is often available. For example, in an experiment where temperature (A) is controlled, then for any quantity B that changes we can infer A causes B. Our new publication in ICML shows how to incorporate such knowledge when inducing a causal models from other datasets.
Is it possible to predict a disease is correlated with an exposure factor without ever performing a study to measure them? The answer is shown to be yes, in some cases, by analyzing existing datasets that measure these two quantities, but not jointly. The idea is to induce all causal models that are simultaneously consistent with several existing datasets and make novel statistical inferences, sometimes regarding quantities never jointly measured together. We have named the approach Integrative Causal Analysis (INCA). A recent important publication in the Journal of Machine Learning Research shows that INCA often makes new and very accurate inferences. It works ubiquitously on all domains we have tried it. It is strong evidence that theories of Computational Causal Discovery can go further than classical statistics: modeling and inducing causal relations gives more inferences capabilities.
The STATegra collaborative FP7 proposal has been accepted for funding by the EC! Our group is a member of the STATegra consortium and will be leading the Work Package related to the development of machine learning, statistical, and data mining methods to integratively analyze heterogeneous biological datasets. STATegra funding will allow us to grow, expand, and perform leading edge research on quite novel machine learning methods, particularly in the direction of Integrative Causal Analysis. This is an ambitious project, excellent consortium and we are very excited to be a part of it. Hiring should start soon.
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