Description
Causal Inference focuses on the application and interpretation of quasi-experimental and graphical models in an attempt to reveal causal structures. The workshop explores the basics of causal inference; including potential outcomes, counterfactuals, confounding, and mediation. The traditional “gold standard” of randomization is discussed as a motivating factor as we evaluate methods for revealing causation in quasi-experimental settings, such as: differences-in-differences, propensity scores, and regression discontinuity. In addition to traditional statistical methodology, we also will focus on graphical approaches such as mediation, directed acyclic graphs, and structural causal models.
