Causal Deep Learning : Now Publishers : 9781638284000 : 01 Aug 2024 : Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often requires crucial assumptions which cannot be tested in practice. This monograph, entitled Causal Deep Learning (CDL), presents a new way of looking at causality. The causal deep learning framework in this monograph spans three dimensions: (1) a structural dimension, which incorporates partial yet testable causal knowledge rather than assuming either complete or no causal knowledge among the variables of interest; (2) a parametric dimension, which encompasses parametric forms that capture the type of relationships among the variables of interest; and (3) a temporal dimension, which captures exposure times or how the variables of interest interact (possibly causally) over time. The CDL framework used enables precise categorisation and compa