Causality has been a burgeoning field, but for researchers it has been difficult to discern the assumptions they have to abide by in order to glean sound conclusions from causal concepts or methods. In an article published in the Journal of Artificial Intelligence Review, HCSS data scientists Maarten Vonk and Nino Malekovic, together with Thomas Bäck and Anna Kononova of the Leiden Institute of Advanced Computer Science (LIACS), aim to disambiguate the different causal concepts that have emerged in causal inference and causal discovery from observational data by attributing them to different levels of Pearl’s Causal Hierarchy.