Computational Methods and Causal Analysis

This course provides a guided exploration of advanced topics in quantitative methods, with a particular emphasis on computational approaches and modern causal inference. It covers cutting-edge techniques including machine learning, deep learning for unstructured data (such as text and images), nonparametric and semiparametric estimation, as well as their integration contemporary frameworks for causal mechanisms and inference, such as double/debiased machine learning and targeted maximum likelihood estimation. Course content will evolve with developments in the field and incorporate recent research from the methodological literature. The course emphasizes both rigorous theoretical foundations and hands-on programming experience.