This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational data within known causal structures. It delves into the characteristics of DSCMs by analyzing the hypotheses, guarantees, and applications inherent to the underlying deep learning components and structural causal models, fostering a finer understanding of their capabilities and limitations in addressing different counterfactual queries. Furthermore, it highlights the challenges and open questions in the field of deep structural causal modeling. It sets the stages for researchers to identify future work directions and for practitioners to get an overview in order to find out the most appropriate methods for their needs.
This paper explores the integration of mobile augmented reality technology in museums, specifically through the development of the "Talkative Museum" prototype, aiming to enrich the museum visiting experience through interactive and collaborative engagements. As vital conduits for cultural and knowledge transmission, museums face the challenge of captivating the digital-native younger generation. Our research addresses this by leveraging advancements in mobile computing and human-computer interaction to foster engaging, intuitive, and collaborative learning environments within the museum context. The prototype facilitates interactions among parents and children and exhibits and encourages exploration and dialogue through augmented reality-enhanced activities. Preliminary user tests involving participants from the National Museum of Ethnology indicated positive responses to the app's usability and collaborative aspects of the museum experience. The analysis of questionnaire responses and observational data suggests that the prototype successfully promotes user engagement, collaborative learning, and the discovery of new knowledge.
