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We explore how observational and interventional causal discovery methods canbe combined. A state-of-the-art observational causal discovery algorithm fortime series capable of handling latent confounders and contemporaneous effects,called LPCMCI, is extended to profit from casual constraints found throughrandomized control trials. Numerical results show that, given perfectinterventional constraints, the reconstructed structural causal models (SCMs)of the extended LPCMCI allow 84.6% of the time for the optimal prediction ofthe target variable. The implementation of interventional and observationalcausal discovery is modular, allowing causal constraints from other sources.  The second part of this thesis investigates the question of regret minimizingcontrol by simultaneously learning a causal model and planning actions throughthe causal model. The idea is that an agent to optimize a measured variablefirst learns the system's mechanics through observational causal discovery. Theagent then intervenes on the most promising variable with randomized valuesallowing for the exploitation and generation of new interventional data. Theagent 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 LPCMCIalgorithm. The numerical results show that detecting and using interventionalconstraints leads to reconstructed SCMs that allow 60.9% of the time for theoptimal prediction of the target variable in contrast to the baseline of 53.6%when using the original LPCMCI algorithm. Furthermore, the induced averageregret decreases from 1.2 when using the original LPCMCI algorithm to 1.0 whenusing the extended LPCMCI algorithm with interventional discovery.

Observational and Interventional Causal Learning for Regret-Minimizing Control