Causal Inference: Design and Analysis

Catalog description

Causal inference seeks to study the causal effect of certain treatment/exposure/intervention on some outcome of interest. It has been widely used in public health, biomedical research, social sciences, educational research, economics, etc. The course will introduce some fundamental and advanced causal inference methods and will emphasize their applications in public health, biomedical research, and social sciences. Topics include the potential outcomes framework, treatment effect models, design and analysis of randomized experiments, methods for adjusting for overt bias in observational studies, sensitivity analysis for hidden bias in observational studies, detection of hidden bias, and methods for controlling for hidden bias. Each topic will be illustrated with extensive real-data public health examples.