On the Use of Causal Models to Build Better Datasets

In recent years, Machine Learning and Deep Learning communities have devoted many efforts to studying ever better models and more efficient training strategies. Nonetheless, the fundamental role played by dataset bias in the final behaviour of the trained models calls for strong and principled methods to collect, structure and curate datasets prior to training. In this paper we provide an overview on the use of causal models to achieve a deeper understanding of the underlying structure beneath datasets and mitigate biases, supported by several real-life use cases from the medical and industrial domains.