The Center for Integrated Latent Variable Research (CILVR)
presents
INTRODUCTION TO GRAPHICAL MODELS FOR CAUSAL INFERENCE
MARCH 31 - April 2, 2021
taught by
Peter M. Steiner, University of Maryland
Short Description
This three-day course is intended as an introduction to the theory and application of graphical models (also referred to as causal graphs or directed acyclic graphs—DAGs). The major goal is to learn about the foundations of graphical models and to use them as powerful tools for designing, analyzing, and critically assessing evaluation studies. The first part of the course starts with a thorough introduction to graph terminology, causal graphs, and structural causal models (SCMs), with a focus on drawing graphs from subject matter theory, understanding collider variables and collider bias, and using the graphical concepts like d-separation to derive testable implications of the presumed causal model. Then, graphical models are used to define causal effects and to discuss identification criteria like the backdoor or frontdoor criterion and the do-calculus. The identification results will be linked to nonparametric and parametric estimation strategies.
The second part of the course highlights useful applications of causal graphs including omitted variables bias and covariate selection issues, alternative causal identification strategies (gain scores/difference-in-differences, instrumental variables), the design of causal studies, counterfactuals, and mediation analysis.
The computing software R is used for graphical and statistical analyses of real and simulated data to demonstrate the entire research process from translating subject matter theory into causal graphs to the analysis of data. For the graphical analysis R packages like dagitty, ggdag, or causaleffect will be introduced.
Course Fees
Professional: $495
Full-time student*: $295
*Full-time students must submit student status proof at https://go.umd.edu/CILVR-STUDENT for prompt processing of the registration.
Free for registered HDQM Department faculty and HDQM degree-seeking students, although you must register through the internal link.
REFUND POLICY: Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.
More details and registration instructions available on the CAUSAL-2021 Workshop page.
For any questions, please contact Patrick Sheehan at graph.cilvr@gmail.com.