Undergraduate Program Status
This course introduces methods and concepts of causal inference from observational data, By exploring the philosophy and utility of directed acyclic graphs (DAGs), participants will learn to recognize and avoid a range of common pitfalls in the analysis of complex causal relationships, including the longitudinal analyses of change, mediation, nonlinearity and statistical interaction.
Upon completion of the course, participants will be able to define causal effects using potential outcomes, describe the difference between association and causation, express assumptions with causal graphs and implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting), and they will be able to identify which causal assumptions are necessary for each type of statistical method.
Homework (10%), Project (40%), Final (50%)