Causal Graphical Models

1 Unit · Graduate

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This courses introduces causal inference methods, primarily using probabilistic graphical models, to identify and estimate counterfactual quantities as functions of observational data. We will discuss common challenges to causal inference, including confounding bias, missing data, measurement error, and selection bias. The final project will allow students to choose a dataset on which to perform a causal data analysis.