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Causal discovery is of utmost importance for agents who must plan and decide based on observations. Since mistaking correlation with causation might lead to un- wanted consequences. The gold standard to discover causal relation is to perform experiments. However, experiments are in many cases expensive, unethical or impossible to perform. In these situations, there is a need for observational causal discovery. Causal discovery in the observational data setting involves making significant assumptions on the data and on the underlying causal model. This thesis aims to alleviate some of the assumptions and tries to identify the causal relationships and causal mechanisms using generative neural... 

With the increasing advancement of technology and available information, finding suitable content for users is one of the significant challenges. Recommender systems have emerged as an effective solution to confront these challenges. Reinforcement learning can be employed as an effective approach to designing and implementing recommender systems. However, there are various challenges and complexities in the context of reinforcement learning applied to recommendation systems. In this context, Online users are treated as the environment, and concepts like reward functions and environment dynamics are not clearly defined, complicating the reinforcement learning process. In this thesis, a...