  We explore how observational and interventional causal discovery methods can
be combined. A state-of-the-art observational causal discovery algorithm for
time series capable of handling latent confounders and contemporaneous effects,
called LPCMCI, is extended to profit from casual constraints found through
randomized control trials. Numerical results show that, given perfect
interventional constraints, the reconstructed structural causal models (SCMs)
of the extended LPCMCI allow 84.6% of the time for the optimal prediction of
the target variable. The implementation of interventional and observational
causal discovery is modular, allowing causal constraints from other sources.
  The second part of this thesis investigates the question of regret minimizing
control by simultaneously learning a causal model and planning actions through
the causal model. The idea is that an agent to optimize a measured variable
first learns the system's mechanics through observational causal discovery. The
agent then intervenes on the most promising variable with randomized values
allowing for the exploitation and generation of new interventional data. The
agent then uses the interventional data to enhance the causal model further,
allowing improved actions the next time.
  The extended LPCMCI can be favorable compared to the original LPCMCI
algorithm. The numerical results show that detecting and using interventional
constraints leads to reconstructed SCMs that allow 60.9% of the time for the
optimal prediction of the target variable in contrast to the baseline of 53.6%
when using the original LPCMCI algorithm. Furthermore, the induced average
regret decreases from 1.2 when using the original LPCMCI algorithm to 1.0 when
using the extended LPCMCI algorithm with interventional discovery.
