  Although standard Machine Learning models are optimized for making
predictions about observations, more and more they are used for making
predictions about the results of actions. An important goal of Explainable
Artificial Intelligence (XAI) is to compensate for this mismatch by offering
explanations about the predictions of an ML-model which ensure that they are
reliably action-guiding. As action-guiding explanations are causal
explanations, the literature on this topic is starting to embrace insights from
the literature on causal models. Here I take a step further down this path by
formally defining the causal notions of sufficient explanations and
counterfactual explanations. I show how these notions relate to (and improve
upon) existing work, and motivate their adequacy by illustrating how different
explanations are action-guiding under different circumstances. Moreover, this
work is the first to offer a formal definition of actual causation that is
founded entirely in action-guiding explanations. Although the definitions are
motivated by a focus on XAI, the analysis of causal explanation and actual
causation applies in general. I also touch upon the significance of this work
for fairness in AI by showing how actual causation can be used to improve the
idea of path-specific counterfactual fairness.
