This module aims to equip students with advanced skills to transform raw data into the appropriate format for statistical analysis and conduct state-of-the-art methods for causal inference from observational data. It involves reading and writing datasets, data cleaning, labelling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of your data, etc. This module also focuses on programming. In empirical analysis, to varying extents, programming could significantly enhance data management efficiency. It covers a range of topics, elementary concepts and tools, functions, macros, scalars, and matrices, loops, etc. Building on this foundation, the module further explores a few advanced methods on causal inference from observational data, such as basics of causal inference, matching methods, differences-in-differences, regression discontinuity and instrumental variables regressions. Finally, students will learn to communicate empirical choices convincingly. This module is tailored to research students across management disciplines and research areas.