Courses Details

BIOSTAT881: Causal Inference and Statistical Methods for Personalized Health Care

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 Credit Hour(s) for residential students;
  • Instructor(s): Wang, Lu (Residential);
  • Prerequisites: BIOSTAT 601, BIOSTAT 602, BIOSTAT 650, BIOSTAT 651, BIOSTAT 653, BIOSTAT 801, and BIOSTAT 802 (concurrent also accepted).
  • Description: This course discusses statistical theory/methodology aimed at addressing causal inquiries from observational data and complex randomized designs, as well as statistical methods for evaluating dynamic treatment regimes for personalized health care. Two key approaches will be focused on: the directed acyclic graph (DAG) and models for counterfactuals (structural models).
  • Learning Objectives: At the end of the course the students will be able to: 1) Formulate causal contrasts of interest for addressing specific scientific inquiries. 2) Derive graphical models for investigating the conditions under which the causal contrasts of interest are identified from data collected under specific study designs. 3) Formulate adequate structural models for making inference about the causal contrasts of interest. 4) Understand the statistical formulation and methods for evaluating dynamic treatment regimes.