  We are not only observers but also actors of reality. Our capability to
intervene and alter the course of some events in the space and time surrounding
us is an essential component of how we build our model of the world. In this
doctoral thesis we introduce a generic a-priori assessment of each possible
intervention, in order to select the most cost-effective interventions only,
and avoid unnecessary systematic experimentation on the real world. Based on
this a-priori assessment, we propose an active learning algorithm that
identifies the causal relations in any given causal model, using a least cost
sequence of interventions. There are several novel aspects introduced by our
algorithm. It is, in most case scenarios, able to discard many causal model
candidates using relatively inexpensive interventions that only test one value
of the intervened variables. Also, the number of interventions performed by the
algorithm can be bounded by the number of causal model candidates. Hence, fewer
initial candidates (or equivalently, more prior knowledge) lead to fewer
interventions for causal discovery.
  Causality is intimately related to time, as causes appear to precede their
effects. Cyclical causal processes are a very interesting case of causality in
relation to time. In this doctoral thesis we introduce a formal analysis of
time cyclical causal settings by defining a causal analog to the purely
observational Dynamic Bayesian Networks, and provide a sound and complete
algorithm for the identification of causal effects in the cyclic setting. We
introduce the existence of two types of hidden confounder variables in this
framework, which affect in substantially different ways the identification
procedures, a distinction with no analog in either Dynamic Bayesian Networks or
standard causal graphs.
