The presented book, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference. It presents thirteen original contributions that span various topics, namely the role of instrumental variables in causal inference, the estimation of average treatment effects and the temporal causal models. Four papers are devoted to the design of novel causal models using interventions. The contributions use approaches of information theory, probability, algebraic structures, neural networks and with them related machine learning tools. The papers range from the theoretical ones, the paper applying the models, to the papers providing software tools for causal inference. We believe that this heterogeneous collection of papers from leading experts extends the knowledge of the community working in causal inference both in theory and practical applications.

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