University of Massachusetts, Amherst | E20 Machmer Hall
A large number of analytical methods have been developed to infer causal dependence from observational data, including propensity score matching, instrumental variable designs, interrupted time-series designs, and many others. Unfortunately, the assumptions and limitations of these methods can be difficult to explain and reason about. This tutorial introduces participants to causal graphical models, a powerful formalism developed within computer science and statistics. This tutorial assumes only a basic understanding of probability and statistics and no knowledge of programming.