Anouncement:

• Problem set 1.1 posted! Check out the description on Sakai 🙂

Key concepts

• Maximum likelihood estimation
• Leverage
• Discrepancy
• Confounding
• Covariates
• DAGs

Lecture 1. Fitting regression models

• Reading
  • GHV ch 8 (8.1 & 8.2, skip “Bayesian inference” in 8.1)
• Lecture contents
  • Clarify any confustions about the instructions of Problem Set 1.
  • Purpose of estimation
  • Review OLS (and discuss its limitations)
  • Introduce one more general estimation method:
    • Maximum Likelihood: How does it work?
    • Note: We will actually use ML later. PROC GLM uses least squares!!
  • Discuss case influence
• Discuss questions on exercise 1.2
• Introduce practice exercise 1.3
  • Go through the syntax?

Lecture 2. DAGs and Causal Inference

• Reading
  • GHV ch 20.1-20.5
  • Wouk, Bauer, & Gottfredson (2020)