The objective was to explore and apply causal inference and machine learning techniques to better understand the relationships between variables in different contexts. The method used involved constructing a causal graph, utilizing the DoWhy tool for performing causal inference alongside machine learning models, and validating the results. Causal inference is the methodology employed to determine cause-and-effect relationships between variables, unlike simple statistical associations. Machine learning is a field of artificial intelligence focused on training algorithms to make predictions or identify patterns from data. DoWhy is an open-source tool that facilitates the implementation of causal inference, providing a framework to define, estimate, and test causal hypotheses. The case studies were Happiness and Cars, where we analyzed the determinants of happiness and the factors influencing the performance and efficiency of motor vehicles. The conclusions were that the combined application of causal inference and machine learning, supported by DoWhy, offers a robust approach to discovering and validating causal relationships in various fields of study, providing valuable insights for data-driven decisions.