PhD Causal Inference and Machine Learning

450B: Causal Inference (W:2021,2022)

TA for Prof Jens Hainmueller

• Visualising identification strategies
• Slides
  • Randomised Experiments
  • Selection on Observables
  • Sensitivity Analysis
  • Difference in Differences
  • Panel Data and Synthetic Control
  • Instrumental Variables I
  • Instrumental Variables II
  • Regression Discontinuity Design

450C: Machine Learning (S:2021,2022)

TA for Prof Justin Grimmer

• Slides
  • Maximum Likelihood Basics
  • Goodness of Fit, Principal Components Analysis
  • Regularised Regression
  • Random Forests and GAMs
  • Text Analysis and LDA
  • Heterogeneous Treatment Effects
    • More advanced treatment for a workshop
  • ML and Causal Inference under Unconfoundedness
    • More advanced treatment including material on continuous treatments and panel data