This course provides you with advanced tools for empirical analysis in economics, focusing on modern econometric techniques used to identify causal relationships. It integrates econometric theory with applied research practice and replication of published studies, equipping you to design and implement credible empirical strategies that allow economists to infer cause-and-effect relationships from real-world data. The course covers key methods in applied microeconometrics, including randomised and social experiments, instrumental variable estimation, difference-in-differences techniques for panel data, regression discontinuity designs, and synthetic control methods. Each method is introduced through a combination of lectures, replication exercises, and discussions of seminal and recent papers from leading economics journals. Emphasis is placed on understanding the assumptions underlying each method, evaluating their validity, and interpreting results critically. A central component of the course is the application of these methods to your own research question. You will design an empirical study, identify relevant data, and apply appropriate econometric techniques to analyse causal effects. By engaging directly with frontier research, you will strengthen your ability to critically evaluate empirical work and apply advanced econometric tools in both academic and professional contexts. After completing the course, you will be able to design and execute modern microeconometric analyses, replicate and extend empirical estimations, and critically assess the credibility of causal claims in applied economic research. You will also be proficient in using statistical software for advanced econometric modeling, preparing you for independent empirical work at the master’s thesis level and beyond. **Connection to Research ** The course is research-oriented and based on recent academic journal articles. You will complete two advanced replication assignments reproducing core results and will also independently develop extensions (e.g., alternative specifications, robustness analyses, or heterogeneity tests). In addition, you will conduct an independent empirical study: formulate a research question, identify and retrieve data, develop a credible identification strategy, implement the analysis in STATA (or other software of your choice, e.g. Python or R), and present the results in an examination seminar. The course is strongly embedded with research practices in applied economics. **Connection to Practice ** The course links econometric methods to real-world policy and business problems. You will work with microdata similar to those used in public agencies, international organisations, and private-sector analytics. Through replication assignments and an independent project, you practice defining problems, selecting data, implementing empirical strategies, and communicating findings—skills directly transferable to professional practice. **Connection to Ethics, Responsibility, and Sustainability (ERS) ** ERS perspectives are integrated through the emphasis on responsible causal inference, transparency, and critical assessment of assumptions and limitations. Issues of data integrity, confidentiality, and reproducibility are addressed in connection with empirical assignments.