  This paper makes a first step towards a logic of learning from experiments.
For this, we investigate formal frameworks for modeling the interaction of
causal and (qualitative) epistemic reasoning. Crucial for our approach is the
idea that the notion of an intervention can be used as a formal expression of a
(real or hypothetical) experiment. In a first step we extend the well-known
causal models with a simple Hintikka-style representation of the epistemic
state of an agent. In the resulting setting, one can talk not only about the
knowledge of an agent about the values of variables and how interventions
affect them, but also about knowledge update. The resulting logic can model
reasoning about thought experiments. However, it is unable to account for
learning from experiments, which is clearly brought out by the fact that it
validates the no learning principle for interventions. Therefore, in a second
step, we implement a more complex notion of knowledge that allows an agent to
observe (measure) certain variables when an experiment is carried out. This
extended system does allow for learning from experiments. For all the proposed
logical systems, we provide a sound and complete axiomatization.
