Elective: Causal Inference

1. Foundations 2. Experiments, Quasi-experiments, and Definitely not Experiments 3. Stratification, Regression, and all that 4. Machine Learning and Big Data Changes Everything 5. More Machine Learning 6. Old School Tools from the Economists (2) 7. Collider Bias in Theory and Practice 8. Mediation, of the Statistical Variety 9. Fairness and Bias in Algorithms and Humans 10. Fairness and Bias: A Case Study 11. Special Topics: Sensitivity and Bounds 12. Special Topics: Alternative Approaches to Causal Inference

Old School Tools from the Economists (2)

Half Time Review, plus some Diff-in-Diff

Taking stock of things now we’re half way through.

Readings

From the slides

Lecture