PREV 724 INTRODUCTION TO CAUSAL INFERENCE AND MACHINE LEARNING
The objective of this course is to equip students with a diverse range of data analysis techniques (such as model-free estimation, model-based estimation, time-varying treatments, causal survival models, causal mediation models, machine learning models, etc.) for estimating causal effects under specific assumptions when data is gathered for each individual in a population. Additionally, the course will delve into practical applications of these methods through the analysis of real-world data and the examination of cutting-edge research in causal reasoning. By the conclusion of this course, students will have acquired a comprehensive understanding of causal inference principles, especially within the context of biomedical studies, and will have gained proficiency in using commonly employed R functions for data analysis. Furthermore, this course will lay a solid foundation for students interested in pursuing further research in causal inference and will serve as valuable preparation for more advanced courses in causal inference field.