Below you will find a general outline of the topics we’ll cover in this course. All other materials regarding homework assignments, readings, and other information will be posted on the course website. Depending on how the class progresses, the order of these topics might change, so check regularly for an updated version of this outline:
| Week | Class Dates | Topics |
|---|---|---|
| 1 | Aug 21 - Aug 23 | Syllabus overview and course motivation; Multiple regression models: Quick review, statistical adjustment. |
| 2 | Aug 28 – Aug 30 | Multiple regression models (cont.): Interactions in regressions; Nonlinear models. |
| 3 | Sep 4 – Sep 6 | Potential issues with regressions: Outliers, heteroskedasticity. (RECORDED CLASS). For students in Monday’s sections, this will substitute the class on Dec. 4th Homework 1 due |
| 4 | Sep 11 – Sep 13 | Introduction to Causal Inference: Potential outcomes framework, the fundamental problem of causal inference, causal estimands, and study design. |
| 5 | Sep 18 – Sep 20 | Randomized-Controlled trials: The gold standard? How can we use RCTs to answer causal questions? Limitations of RCTs. Internal and external validity. A/B testing. Homework 2 due |
| 6 | Sep 25 – Sep 27 | Introduction to Observational Studies: Can we use data outside experiments to make causal claims? Matching and its comparison to RCTs. Overt and hidden biases. |
| 7 | Oct 2 – Oct 4 | Observational studies: Using real-world variation to estimate causal effects. Natural experiments, Diff-in-Diff. Homework 3 is due |
| 8 | Oct 9 – Oct 11 | Observational studies (cont.): Regression discontinuity design (RDD). |
| 9 | Oct 16 – Oct 18 | Midterm exam |
| 10 | Oct 23 – Oct 25 | Introduction to prediction; Building models: Bias-variance tradeoff; out-of-sample validation and cross-validation. |
| 11 | Oct 30 – Nov 1 | Model selection and regularization: Lasso and Ridge regression. Homework 4 due |
| 12 | Nov 6 – Nov 8 | Introduction to Prediction models: Classification and regression trees (CART). |
| 13 | Nov 13 – Nov 15 | Prediction models (cont.): Random forests, boosting, and ensemble methods. Homework 5 due |
| 14 | Nov 20 – Nov 22 | THANKSGIVING BREAK |
| 15 | Nov 27 – Nov 29 | Bringing it all together Homework 6 due |
| 16 | December 7 (8-11AM) | Final Exam |