LFU:online

•   Acquisition of profound knowledge about modern methods of causal inference and            policy evaluation, their identifying assumptions, and fields of application.
•   Application of econometric and machine learning methods to investigate causal                effects and evaluate policies using real-world data.
•   Implementation of empirical analyses with statistical software, in particular R, using         relevant packages for causal inference and machine learning.
•   Development of competence to understand, assess, and apply state-of-the-art causal       and machine learning methods as used in contemporary empirical economics.

The course provides an introduction to modern methods of policy evaluation and causal machine learning, focusing on the interplay between econometric identification strategies and data-driven estimation techniques.

Topics include:
•    Foundations of causal inference 
•    Randomized experiments and quasi-experimental designs (regression, matching,              inverse probability weighting, doubly robust methods, instrumental variables,                  difference-in-differences, synthetic controls, regression discontinuity designs)
•    Machine learning methods for causal inference such as double/debiased machine              learning 
•    Heterogeneous causal effects and individualized policy evaluation, including optimal          policy learning  

All methods are illustrated with practical applications in R, drawing on examples from economics

Causal Analysis, Impact Evaluation and Causal Machine Learning with Applications in R, 2023, by Martin Huber