  Research around AI for Science has seen significant success since the rise of
deep learning models over the past decade, even with longstanding challenges
such as protein structure prediction. However, this fast development inevitably
made their flaws apparent -- especially in domains of reasoning where
understanding the cause-effect relationship is important. One such domain is
drug discovery, in which such understanding is required to make sense of data
otherwise plagued by spurious correlations. Said spuriousness only becomes
worse with the ongoing trend of ever-increasing amounts of data in the life
sciences and thereby restricts researchers in their ability to understand
disease biology and create better therapeutics. Therefore, to advance the
science of drug discovery with AI it is becoming necessary to formulate the key
problems in the language of causality, which allows the explication of
modelling assumptions needed for identifying true cause-effect relationships.
  In this attention paper, we present causal drug discovery as the craft of
creating models that ground the process of drug discovery in causal reasoning.
