Causal Learning in Biomedical Applications

Understanding causal models is important in a number of fields, from healthcare to economics, as it allows for precise forecasting a training of reinforcement learning. Learning causal models involves extracting potential non-linear relationships and dependencies between variables from sampled time-series. For example, modelling of biomarkers of non-communicable disase as a function of diet and action monitoring has shown the potential of being a powerful tool to guide the recommendations on healthy diet. We aim to learn causal models that extend basic causal models in several directions, which are relevant in biomedical appliations. In particular, such causal models need to handle nonlinear causality, such as the competition among bacterial strains for metabolites. Likewise, one may consider constraints that become active beyond certain thresholds. Dealing with latent variables, such as hormone concentrations, is a challenge that demands a balance between model complexity and interpretability. These models should also consider cyclic relationships, time-series dynamics, and mixture-model aspects, accommodating individual variations in metabolism without predefined subgroups.