Causality

• An understandable explanation must be created by a machine in a given time (e.g., one hour or one day) and can be comprehended by a user, who need not to be an expert, but has an educational background
• The user keeps asking a finite number of questions of the machine until he/she can no longer ask why or how because he/she has a satisfactory answer; we say he/she has comprehended.
• This is the relationship between cause and effect; it is not a synonym for causability
• Causability is about measuring and ensuring the quality of an explanation and refers to a human model
• causality requires a wide frame of prior knowledge to prove that observed effects are causal
• A ML model only discovers correlations among the data it learns from, and therefore might not suffice for unveiling a cause-effect relationship.
• However, causation involves correlation, so an explainable ML model could validate the results provided by causality inference techniques, or provide a first intuition