Causal modeling allows predicting a system’s behavior not only under observation but also under intervention. Computational causal discovery reverse-engineers causal models (networks) from observational data with limited or no interventions. In this work, I will present logic-based causal discovery, a new, versatile approach for learning causal networks from observations and interventions: based on standard causal assumptions, associative patterns in the data that constrain the search space of possible causal models are expressed as a logic formula. Truth-setting assignments to this formula correspond to causal networks that fit the data. This approach can reason with multiple data sets, handle conflicting statistical information, and produce novel, non-trivial predictions. I will also discuss possible applications and future extensions, aiming to answer specific causal questions in biomedical informatics.​